So, today we will discuss issues in scaling gate oxide. Now, first we will address S I O 2 scaling will see that you know silicon oxide has been fairly good insulator on silicon probably the best insulator semiconducting combination that one can find in the nature. But we see that there are now issues in using silicon oxide in transistor structure right. We will take a look at that you know we will also talk about the reliability issues of gate oxide. In particular we will look at two concepts one what we call f n tunneling that is to say if you start applying higher and higher voltages on silicon oxide that is start increasing the electric field at some point you will start conducting through the silicon oxide. And that phenomenon is called f n tunneling which is essentially an abbreviation called Fowler Nordheim tunneling. So, we will look at that and we will also study what is meant by T D D B which stands for time dependent dielectric breakdown. So, then let us get started with you know understanding the silicon oxide and you know what issues are we talking about here and you know when we look at silicon and silicon oxide this Si O 2 we say is thermally grown. What it means essentially is that you know you subject the silicon wafer expose the silicon wafer to high temperature such as you know greater than let us say 900 degree centigrade and expose it to oxygen ambient. And this would result in conversion of silicon into silicon oxide you know that is what we call growth of silicon oxide as opposed to depositing something on top of silicon oxide right we actually convert silicon into silicon oxide. As you could well imagine at this interface there is a transition what we have in silicon is that we have a nice covalent bond structure it is a crystalline semiconductor as you know and silicon oxide is an amorphous material and essentially you know what you have in silicon oxide is essentially silicon bonding to oxygen right. So, you know you have Si is essentially bonded to oxygen and this will essentially continue on right in other words you know this there will be another O and silicon and O and silicon and so on and so forth right. So, that is what you have in other words an oxygen atom is shared between two silicon atom right. So, that satisfies the valence and hence a silicon atom is bonded with 4 half oxygen atoms which is essentially Si O 2 you see, but this is not crystalline you know this is really you know very regular right it would not be looking like that. But the point is that every silicon oxygen atom will bond to an oxygen atom and this is how you have a structure in silicon oxide in bulk silicon oxide that is that is when I start growing oxide on top of silicon as I go deeper and deeper up thickness is increasing essentially you see a very ideal silicon oxide structure where you know silicon is bonded to oxygen which is in turn bonded to silicon and you have this mesh. But at this interface you see you have a transition right you are transitioning from one material to another material right and this is what is extremely crucial in the context of field effect transistor. And the issue at this interface is that you know you have two important aspect here one is what is called interface traps and the other one is what is called oxide charges. Sometimes they are also called fixed oxide charges you know they are sort of denoted as Q F to say this some charge which is fixed it does not change and you have what is called interface traps and typically the inter when I say Q F the typical unit that we are talking is you know coulomb per centimeter square you know mostly we talk of per unit area. And we also sometimes refer to the number of traps fixed traps in the oxide which is essentially number per centimeter square and when you have multiply this with Q you get Q F that is essentially transition from number to the charge. Now similarly you have interface traps interface traps are typically denoted as the interface trap charge is denoted as Q I T the traps themselves are denoted as N I T which is exactly at this interface whereas these oxide charges are little bit away from the interface right this is where you will find Q F and this is where you will find Q I T Q I T is exactly at the interface and that is the distinction essentially. And as you start going away then the oxide is fairly ideal you know there are no charges in an insulator you know all that is fine. And Q I T as opposed to fixed charge can be either negative positive right and that depends on whether you are populating holes in the channel or electrons in the channel whereas Q F is always fixed it does not change and in a silicon oxide system which is charge inside the silicon oxide Q F is always positive. And it is conjunctured that Q F arises at this interface because when you go little away from interface because you still do not have a regular oxide structure in other words more importantly it is conjunctured that you have a mix missing oxygen atom in that region. So, this is also called oxygen vacancy because this is transitioning from silicon to silicon oxide. So, it is not quite S I O 2 it is S I O X where that X is less than 2 right in some places you have missing oxygen. And it turns out such a defect what it does is that it loses an electron right because now there is no oxygen there is one electron here and one electron here it loses one electron out and it gets positively charged. And this is what is supposed to give rise to all the positive charge that we see in silicon oxide at very close to this interface. And this is fixed because this is little bit away from the interface what you do in the channel has no bearing on changing this because you cannot have this carriers go in and change it state that is why it is called fixed. On the other hand at the interface what happens interface state is presumed because at the interface let us say there is a silicon atom sitting here. This silicon atom is bonding to a silicon atom here a silicon atom here as is shown a silicon atom inside down here, but there is a missing silicon here. And in fact this is what we call dangling bond if the silicon were to continue up you will have another silicon atom sitting there. And you know it is a perfect covalent bonding mechanism, but here you know you will have lot of these missing silicon atoms exactly at the interface where the silicon is sitting at this interface, but it has failed to find another silicon atom here because it is oxygen rich it is silicon oxide right. And hence that gives rise to interface state this interface traps and oxide charges they can make or break your device. If you have very large amount of interface traps and oxide charge there is no way you can build a field effect transistor. This is the precise reason why even though we had known about field effect transistor theory way back in 1930's a field effect transistor on silicon was only formed in 1960's because it was very difficult to create an oxide which has very low fixed charge very low interface state charge the technology was not mature at that time. That is why the first semiconductor transistor was a bipolar junction transistor right it was 1947, but then as the technology progress we came up with lot of techniques to make sure that the oxide which is grown on top of silicon is very high quality oxide which has as low missing oxygen as possible. So, very low oxide charge and as few dangling bonds as possible hence very few interface states. Today you know in fact in the state of the art CMOS technology the numbers that we talk about for NIT is less than 10 power 10 per centimeter square and similarly for NF number per centimeter square is again less than 10 to the 10 per centimeter square. You see this number does not make much sense unless you put this in a perspective what is that perspective let us recall that the silicon atomic density right it is about 5 into 10 power 22 per centimeter cube that is per centimeter cube of silicon you take start counting the atoms you will count so many atoms right that is the atomic density of silicon. Now just a zeroth order calculation for you if this is my silicon silicon if it were infinite in all dimensions then everywhere you go you have this atomic density volume density, but when I cut silicon when I make a silicon wafer at the surface you see this is a silicon surface this is where I am going to grow oxide build devices here we do not talk of volume density here instead we talk of a real density correct because now it is a plane because there is nothing else on top of it to talk of volume density. So, just a zeroth order way of getting the aerial density from a volume density is essentially take this number 5 into 10 to the 22 two-thirds of it is your aerial density you can go back and think about how it is two-thirds, but you know take it for granted from you know it is a 2 d we are looking at instead of 3 d and hence it is two-thirds of that number. So, if you do this math what you get is 5 this is now per centimeter square correct. So, this is approximately 10 to the 15 per centimeter square now you put things in perspective what is it telling you if you had this silicon surface which is a silicon wafer at the surface you will have so many silicon atoms and obviously you will have so many traps because all these have dangling bonds because they are failed to get a silicon on top. So, this is your surface trap density and you could bring this 5 into 10 to the 15 to 10 power 10 per centimeter square that is 5 orders of magnitude decrease is possible because we put silicon oxide on top of silicon that is why even though it is not silicon on top of that surface we say silicon oxide is an excellent passivation layer what it means is that when I grow silicon oxide the silicon oxide silicon chemistry the interface chemistry if you will is so nice that silicon oxide can saturate so many dangling bonds and really bring down the volume I mean the surface trap density by at least 5 orders of magnitude in today's devices. So, that is the kind of you know achievement that we have, but this is just simply not possible if you look at other semiconductors like gallium arsenide you put an insulator on gallium arsenide you will struggle to get anywhere close to 10 power 10 it could be 10 power 12 10 power 13 you know depending on what kind of insulator you have same with germanium oxide if you want to use germanium for making field effect transistor right. And the worst thing about germanium oxide is germanium oxide is water soluble let alone be strong in resisting various chemicals that we use during semiconductor processing after growing germanium oxide even if you wash your germanium vapor it just dissolves germanium oxide right. The key then for a insulator in FET structure FET insulator that is gate insulator is that it should withstand first of all harsh processing because after putting oxide as we have already seen in a CMOS flow we have so many processes it should withstand all those process conditions and give very good interface property and this can be done with silicon silicon oxide. And it is just impossible to do it with any other you know means and that is why and again you know we take lot of care in really achieving that right for example silicon wafer has to be cleaned it has to be cleaned using various chemicals we have now a cleaning procedure called RCA cleaning it is a very well established chemical cleaning procedure where in silicon wafer is put in acidic and alkaline solutions to get rid of all kinds of contaminants metal contaminants organic contaminants and so on and so forth right cleaning is extremely important. And then oxidation at high temperature in is important you know during oxidation you need to really have a very clean ambient you need to have a very high quality quartz furnace in which you can actually do this oxidation. So, oxidation at high temperature and in clean condition and then of course you know you have to immediately put the gate electrode and then we have also figured out immediately after oxidation it also ways helps if you also anneal it in inert ambient like nitrogen you know if you anneal it at high temperature it improves the quality of the interface. And then once you make all your MOS capacitor put the gate we also do what is called forming gas anneal forming gas is essentially a mixture of nitrogen plus hydrogen about 90 percent hydrogen and 10 percent hydrogen and 10 percent hydrogen. The idea here is that you know you have these dangling bonds most of them are passivated by oxygen of silicon oxide, but there still could be some remnant dangling bonds if you expose it to this kind of an ambient hydrogen is a very light material and very reactive material it can actually come in diffusion and if there are any dangling bonds which look like this hydrogen can come and terminate that. And that is the idea of you know doing this forming gas anneal way back in 60s or even before that we did not know all these tricks it took a while to really come up with all these process development technology development and today we have very standard recipes you use this recipes you are guaranteed to get oxide charges less than 10 power 10 per centimeter square. If you have those kind of charges you will have very little effect on threshold voltage your threshold voltage will not deviate from what ideal theory would predict. Otherwise these oxide charges themselves will influence your threshold voltage very drastically. So, this is why we started using silicon oxide, silicon oxide, but a lot of things have happened in silicon oxide you know in terms of our very early MOSFET way back in 1960s which were still large channel MOSFET like 10 micron 20 micron kind of MOSFET. They used to operate at 10 volt and their typical gate oxide thickness of the order of 100 nanometer. And today we talk of 1 volt supply voltage but oxide thicknesses of the order of 1 nanometer. You see something interesting here this is exactly what I had said in the context of scaling theory also constant electric scaling field theory says you keep electric field constant, but if you see the history electric fields have increased over generations and that is very evident voltage has decreased by 10 x whereas thickness of the oxide has decreased by 100 x. If you have to say the electric field is approximately V G by T ox which is V G here 10 volt. You do 10 volt by 100 nanometer you get an electric field which we typically represent in mega volt per centimeter 1 mega volt per centimeter that is the electric field. And today if you talk of 1 volt across 1 nanometer the electric fields are 10 times large 10 mega volt per centimeter. This is precisely the reason for you know concerns today on silicon oxide. And of course the breakdown strength of good quality silicon oxide breakdown strength meaning at this voltage instantaneously it will breakdown you know is of the order of 18 mega volt per centimeter this is still much higher than 10 mega volt per centimeter. But as we will see little later there is a phenomenon called time dependent dielectric breakdown under which even if you have lower electric field below the breakdown so called breakdown strength. You can still have an oxide breakdown over long time that is why it is called time dependent breakdown not instantaneously whereas 18 mega volt instantaneously you have a breakdown we will talk about that in a while. So you see first of all you know in terms of computing electric field across the oxide this is an approximate expression you see really the electric field should be given by as you can very well understand V G minus V F B remember V F B is like a built in voltage there is already that built in voltage due to the fact that the gate work function and substrate work function are not necessarily the same that gives rise to this built in potential which we call here V F B minus V silicon what is V silicon it is essentially psi s the band bending because remember I have already talked about this simple model that is you have C ox C silicon and you apply V G this V G drop across C ox which is called V ox and drops across silicon which is called V silicon. So in a series circuit as you can see total V G is equal to V ox plus V silicon. So your electric field across the oxide is V ox by T ox. So your V ox in turn is V G minus V silicon by T ox but because there is this built in potential as I was telling you there is a V F B term also. So this is the exact expression that you should always use whenever you are asked to compute the electric field. So the key here is that it turns out this electric field is very important as you start increasing the electric field something interesting starts happening in fact you can start seeing significant current through the oxide. Oxide is supposed to be an insulator which is true when you have low electric field but at high electric field just as you have a lightning and air is a insulator supposed to be but you can have the current passing through this medium which is supposedly insulator because you have a very high field generator. And so is the case with silicon oxide also under low field electric condition it is indeed an insulator but beyond certain electric field it can start conducting. And let us look at that particular aspect and understand how does silicon oxide conduct. Let us take for instance case where you know I have it is true whether it is n type or p type. So let us take any one of these conditions let us take n type and I have this SiO 2 and I have gate electrode. It could be let us say poly silicon n plus or p plus if it is a p channel transistor that you are going to build you will put a p plus silicon or you could also have a metal gate it does not matter some conductor is there on the gate. Now let us look at this system and let us consider a case when I apply a positive voltage I mean just for simplicity let us say I just take a metal electrode put aluminum electrode it does not matter what electrode is just let us take that condition. So what happens when you start applying positive voltage as you can see here positive voltage will start accumulating the more and more electrons here correct that is the accumulation condition a negative voltage will drive away electron and it will create a inversion condition. Let us now consider a positive V G condition. So if you look at the band diagram under this condition what you will see is the following. You have an insulator this is a SiO 2 and this is your gate. This is the gate that is aluminum gate and this is your SiO 2 and here this is n type it is accumulating here more electrons at the surface because I have applied positive voltage and accordingly the Fermi level on the gate is lower than the Fermi level on the substrate that is indicative of that this voltage is positive with respect to this voltage. So the band bending will essentially look like this correct there are lot of electrons here in the channel which is here as you can see but these electrons cannot go in because oxide as you know is an insulator what does it mean there is a huge barrier here which we have computed earlier which is greater than 3 electron volt that is a large barrier. So oxide you know this carriers will not tunnel through and hence you have zero current. Let us now consider a case when V G starts increasing more and more positive. What happens when you start applying more and more positive voltage essentially what you have is that you know these two Fermi levels start separating that is the difference between this Fermi level and this Fermi level is the applied voltage and larger voltage will also set up larger electric field which is not difficult to understand and if you recall our discussion may be in the first or second lecture electric field is always related to gradient in band diagram remember this and more precisely 1 over q d e by d x. So what does it mean what is the gradient in band diagram this is a gradient in band diagram correct d e by d x. If I go to larger voltage larger electric field should set up in other words my band bending now will look like this this is a condition for larger electric field what will happen now is correct this is how the band diagram will look this is the difference between this point and this point which is same as this this is the difference between this point and this point which is same as this what has happened in the process is I have separated this Fermi level from this Fermi level by larger voltage and there is a larger gradient this is d e by d x which is your electric field more electric field increase it further you will have even more electric field something like this now there is something very interesting happening if you can see here earlier these electrons had to surmount this barrier to come into the oxide to conduct current. Now if you continue to increase this further and further let us say you have this kind of a situation very large electric field now you see something interesting here there is an electron sitting here and it sees an allowed energy state here which is separated by an extremely small distance what is this distance that distance is governed by electric field larger the electric field I have more very severe band bending and smaller is this distance and when this distance comes to of the order of nanometers then you can have large tunneling current electrons need not go over the barrier electrons can easily tunnel through this so called triangular barrier. So, if you increase it further electric field this distance decreases tunneling distance increases it turns out the tunneling current is an exponential function of a tunneling distance if you decrease the tunneling distance by a small amount this will be an exponential increase in tunneling current then you will see that your tunneling current starts going up exponentially with applied electric field or applied voltage and this is exactly what is called f n tunneling for example, when will this tunneling distance be 1 nanometer just to tell you this which is not very hard to understand right you know this let say if this barrier is 3 volt 3 electron volt then you should have an electric field which is 3 volt per nanometer if you have an electric field which is 3 volt per nanometer you will reach this point which is in line with this point over a distance of 1 nanometer. So, those are the kinds of field that we are talking about that is when the tunneling will start right typically it turns out you will start tunneling even before you reach 1 nanometer 4 nanometer 5 nanometer you know you easily will start seeing significant tunneling current and we typically say that if the electric field are more than you know of the order of 5 to 6 mega volt per centimeter then you will certainly start seeing you know significant tunneling current. So, what is this electric field this is 6 into 10 to the 6 mega right m here and centimeter which is how many nanometer right 10 to the 7 right 10 to the 9 nanometer is a meter and 10 to the 7 is essentially you know this. So, in fact, you can see here that right it is essentially 6 by 10 which is 0.6 volt per nanometer right. So, even when you reach 0.6 volt per nanometer what is 0.6 volt per nanometer for 3 volt barrier to be aligned here you have a tunneling distance of 5 nanometer you see 0.6 times 5. So, typically when you are tunneling distance is of the order of 5 nanometer you will start seeing tunneling current. So, this 6 mega volt per centimeter really corresponds to 0.6 volt per nanometer which approximately corresponds to a tunneling distance of 5 nanometer tunneling distance approximately and this is what you see if you were to look at current density j as a function of electric field e initially your current is very very very small. But, once you start reaching the fields that I was talking about 6 mega volt kind of field you know you see that current starts increasing this being a log scale you see this is a linear line meaning it is an exponential increase with respect to increasing electric field. And in fact, when you reach 10 mega volt per centimeter this is e in a 10 mega volt per centimeter your typical tunneling current density in silicon oxide silicon system are of the order of 100 ampere per centimeter square that is the kind of current density that we will see. The 100 ampere per centimeter square number may look huge, but you need to put things in perspective let us say if you have a you know 1 micron square area for your transistor or a gate area is 1 micron square then you can see that what current you will have. Now, you are 100 ampere per centimeter square and you are talking of 1 micron square. So, this is 100 into 10 to the minus 8 correct and you know that is the kind of current that you will see which is about a micro ampere current, but that micro ampere current is now significant in the context of the transistors that we are talking about. And if you continue this further it continues to increase and if this is really very large you know what is that large of the order of 18 mega volt per centimeter. Again this number may vary depending on how good is your oxide have you done an excellent quality oxide or it is little bit inferior it may be little better than 18 or little less than 18, but that is a approximate range. At this point you see a huge current you know as soon as I go to that voltage I will instantaneously have a very large current it will result in thermal runaway and even metal can melt and it will cause a permanent short in the device. So, that will be a destructive breakdown, but even before this destructive breakdown this is really destructive even before that you can still have reasonable current flowing through the oxide. It is no longer an insulator in this electric field regime that is the main message that I wanted to you know convey to you right. Oxide can conduct if you have sufficiently large electric field and that is because of this tunneling process. And in fact it is called Fowl-Nordheim tunneling because they were the first to really you know sort of give the theory for such tunneling. And in fact you can reasonably calculate this tunneling current reasonably well using this expression which is called Fowl-Nordheim expression. This is E is electric field here and you have this exponential of various quantity let me just write it down for you this is M ox which is a effective mass of electrons in oxide. And where your A is essentially an F N constant which is again given by this expression 8 pi h phi b. Here E is this electric field that we are talking about. You see this exponential dependence on electric field and this is negative because which essentially means that as you increase E the current here increases this is in denominator you see. And E is electric field which is obvious Q is charge and H is Planck's constant and what else do we have M ox is what is called effective electron mass in oxide which is lower than its free mass free electron mass. And phi b is important quantity and that is called barrier height this phi b is barrier height. And what is that barrier height at that barrier height is essentially this that we are talking about this is phi b. Larger the barrier lower will be the current that is not again hard to understand because there is a huge barrier for the carriers to conduct. Smaller the barrier higher is the tunneling current again it is an exponential function of barrier height. Similarly, given a barrier height for example, silicon oxide system barrier height is fixed then you start changing electric field again it is an exponential function of electric field. So, using this expression you can very well compute the tunneling current density. And now let us look at you know the other important the important point again I want to stress is that the oxide current through the oxide is essentially dependent on the field across the oxide. And the barrier height whether injection is from here to here or here to here if the same barrier height exists and same electric field is there the currents will be similar. In other words they are independent of from which side the current is coming into the oxide. But what is crucial is the barrier height and electric field in this context it may be useful to sort of mention here the difference between n channel and p channel transistors. Remember n channel transistors have p type substrate and n plus poly silicon and they are mostly operated with positive gate voltage. That is we are interested in this kind of a tunneling. We have a positive voltage when you invert this transistor with the positive voltage there are lot of electrons and these electrons can actually tunnel through. So, the barrier height here for electrons tunneling is what is important. Now if you look at a p channel transistor you see this is p plus poly silicon and it is operated with negative gate voltage. When you apply negative gate voltage I also create lot of holes here. I also create lot of electrons here and these electrons tunnel. Now you can have two possibilities. One is tunneling of electrons from p side if you are only talking of electrons tunneling. Because this is negative electrons have to go in this direction. But turns out this may not be very significant for two reasons. First of all it is p plus semiconductor. So, availability of electrons itself may be less. But more importantly the barrier height now when we talk of barrier height you know it is the electrons sitting here these have to tunnel into the oxide. This is a huge barrier height now because not electrons sitting in the conduction band we are talking of electrons sitting in the p plus layer. So, that is why this electron current is less and there can be hole current. But again the barrier for hole injection is larger if you recall our discussion when we sketch the band diagram of oxide silicon. I told you that the band offset for electrons is of the order 3 electron volt. Whereas for holes it is of the order of 4 electrons volt at the bottom. So, as an effect the leakage tunneling leakage current in p mass could be less than tunneling leakage current in n mass. So, that is J leakage due to tunneling remember we are not talking of sub threshold leakage we are talking of tunneling leakage is essentially for p mass typically is less than n mass that is because of these considerations. So, at least this is one region where p mass would help you a little bit it would not conduct so much leakage current through the gate oxide. Now let us talk about this issue of time dependent dielectric breaker. We said that if your electric field is greater than 18 mega volt per centimeter there is instantaneous breakdown. But let us say that e is less than 18 mega volt we do know that even when e is less than 18 mega volt per centimeter there can be conduction. As soon as I go more than 5 6 mega volt per centimeter we said that there is a follow not I am tunneling current because tunneling distance has come down to 5 nanometer of that order correct. So, in other words if you look at the band diagram of the oxide what you will see is you know again let me just consider the case where you have n type on the silicon and I have applied you know positive voltage on the gate and as I have already told you this band bending will essentially you know increase and will result something like this correct eventually this right. Now it is less than 18 mega volt but still there is tunneling because this distance is less than 5 nanometer of the order of 5 nanometer now. So, what happens really now is that these electrons come into the oxide conduction band there is large electric field they get accelerated because of the electric field you know it means that their energy increases but they can essentially collide with silicon atoms and they can they can lose their energy and they can go through a series of these processes and eventually they are collected at the anode from cathode they are coming and collected at the anode and that constitutes a current for you. But what can happen is that these electrons which have high energy when they impact with silicon oxygen SiO 2 silicon oxygen bond they can actually break the bonds. In other words these high energy electrons in SiO 2 can break SiO bond SiO bond has certain strength if the electron energy is more than that strength and it collides with that it can break SiO bond and in other words it will start creating defect in SiO 2 correct SiO 2 which was ideal now start seeing lot of defects and in fact we also call this over time these defect starts increasing and this is also referred to as in literature oxide wear out you know just like any mechanical device wears out over long usage you know this oxide over long usage is wearing out you know slowly the bonds are breaking in the oxide and you know what happens because of that is that you know once you start breaking the bonds these are defect sites you know they do not necessarily are like insulating materials you can model that as you know there is some defect here and you know this defect is propagating something like this depending on how the bonds are getting broken and this is what is called a percolation path eventually you know what happens is that this along this path there is more and more electric field there is some kind of a positive feedback more carriers can go in and eventually you can reach a breakdown condition you will have a path a defective path from anode to cathode which is not really an insulating path in other words if you look at the top view of the silicon oxide this is the silicon oxide that I have and this is the silicon material I have right this is the gate silicon oxide and silicon right. So, there is this electrons going and you know they are going of course in all direction but anywhere here a weak spot starts wearing out and eventually in that spot you know there could be a defective path from the gate to the substrate or anode to the cathode and hence you have a destructive breakdown under that condition. So, what it means is that although I had very low electric field which is much lower than the breakdown strength over the time because of the oxide wear out oxide will break down in other words if you actually look at as a function of time current gate current or oxide current you will essentially have some current which is small current as dictated by Fowler-Nordheim tunneling theory given an electric field there is a current you know this current continues like this but over some time you know you will see an effect something like this you can see a huge current flowing in and this is what we call time to breakdown d B D. So, even though I am operating the transistor at a normal operating condition which is much lower than 18 mega volt per centimeter one volt across one nanometer is 10 mega volt per centimeter approximately because you need to do V G minus V F B minus V silicon by T ox but it will be very close to 10 mega volt per centimeter. So, even at 10 mega volt per centimeter which is the normal operating condition of the transistors to in today's chips with silicon oxide as a gate dielectric over time it will break down if you apply larger electric field this will break down even earlier if you have a lower electric field this will break down much later. So, this is why it is important to characterize the so called oxide reliability oxide is insulator it is a good material but over time it breaks down does it break down in 1 year 10 year you cannot of course wait for 10 years to figure out when does it break down right because even before you ship the products in the market you need to make an assessment of the breakdown of the oxide breakdown time of the oxide in other words it is called a reliability prediction. So, the way the reliability prediction is done in all these devices is through what is called accelerated testing. So, what it means is the following right let say I am looking at V across the oxide or V G what that you apply on the gate as a function of life time let me call it as tau L which is a life time of the device and this is really going to operate at let us say 1 volt that is the condition for let us say 65 nanometer or 45 nanometer CMOS technology but for this 1 volt it may survive for 10 years but I cannot wait for 10 years before I ship the product in the market. So, what I do in the fab once I make my device is intentionally apply large electric field much larger electric field than the device will ever see in the field. So, instead of 1 volt I may apply 5 volt let say when you do this this is what I mean by accelerated testing the breakdown breakdown times come down very drastically which is manageable you can do the measurement in the lab you do not wait for 10 years for that. So, you figure out what is the breakdown time may be do it for a few different voltages do it for 6 volt 5 volt and 4 volt and so on and so forth. And based on that you do what is called an extrapolation for used condition in other words what you have really done this is the real test at high fields in other words I accelerate the deterioration and get the trend of it is life time dependence as a function of different voltages which are very high. And I assume that the same phenomenon is valid even at low voltage the wear out phenomenon and try to do an extrapolation and say look if you are going to use this device in the field now this device is going to survive for 10 years or whatever you know you get some life time and this is what is called life time prediction. So, I do the test for a month or so because you know I may engineer this voltage such that you know the whole data should come to me in a month this life time may be month this may be 2 weeks a week or whatever it is right. So, based on that I can now do this extrapolation and this is how this life time prediction engineering is done in foundries. This is an important exercise and the point again to summarize is that even the oxide has an 18 mega volt instantaneous breakdown field it breaks on instantaneously at lower breakdown field which is an operating voltage condition it will still breakdown over a long time and what is that time is to be estimated using this time dependent electric breakdown measurement. And there are various techniques that are used to enhance the reliability of silicon oxide you know silicon oxide reliability is typically very thin silicon oxide also are annealed in either ammonia or nitrous oxide this is actually called nitridation it is not quite silicon nitride, but you first create a silicon oxide matrix if you anneal it in nitrogen nitrogen is very inert you cannot really react nitrogen with silicon, but ammonia or N 2 O if you expose the silicon oxide after oxidation process then some of the S I O bonds can be replaced with S I N bonds and that is why it is called nitridation. And it turns out silicon nitrogen bonds are much stronger than silicon oxygen bonds and hence they can sustain larger electric field. And the other there is also other model that is used to explain the enhancement of the breakdown field and that is essentially when you grow silicon oxide on silicon silicon oxide always have compressive stress. When you put some convert some of the silicon oxide into some S I N bonds it is called silicon oxynitride this is called nitridation this is actually called C I O X N Y where X is very close to 2, but not quite 2 Y is little more than 0 that is why it is called S I O X N Y or it is called silicon oxynitride it turns out nitride typically has tensile strength tensile stress. So, when you put some convert little bit of oxygen oxide into nitride you reduce the compressive stress that is another theory. So, you minimize the stress if the bonds are under stress it is easier to break the bonds if you reduce that stress it is more difficult to break those bonds and that is another theory which is also proposed to explain the fact that when you do the silicon oxynitride you have enhanced reliability. The point that I am again trying to make is that the silicon oxide has been used may be up to 65 nanometer kind of silicon technology after that we have made a migration to high k gate dielectric which we will discuss in the next class next lecture. And when we were doing silicon oxide of the order of 1 nanometer it was really silicon oxynitride because it is so thin reliability is a concern we were actually converting part of that oxygen silicon bonds into nitride nitrogen silicon bonds very very small nitrogen by the way the nitrogen that we were introducing where may be 3 to 4 atomic percentage specifically at the interfaces 2 interfaces because that is the key because the damage starts occurring at the interface right you really need to strengthen the interface. So, thereby you know we have been using this silicon oxynitride until very recently before we made a transition to high k gate dielectric. So, let me summarize then silicon oxide first of all is an excellent material because it passivates the traps at the silicon silicon oxide interface which we have already seen. And that is why silicon silicon oxide system is an excellent system to make FETs unlike germanium or gallium arsenide system that is why silicon has simply displaced all other semiconductors in the industry more than 90 percent of your electronics which is available in the commercial domain is silicon electronics that is because of FETs and that is because of silicon oxide. Silicon oxide has really served us for you know almost 5 decades from 60s till you know very recently that it is a very large service that it has provided. But over the time because we scaled this from 100 nanometer to 1 nanometer we started having this reliability problems first of all also follow nadam tunneling which induced you know this breakdown time dependent breakdown and so on and so forth. And next lecture we will also see there is another phenomenon called direct tunneling that is even more serious. And because of that now we are talking of replacing silicon oxide with high K gate dielectric. So, with that let us conclude the lecture today and in the next lecture we will start discussing about direct tunneling in silicon oxide, ultrathane silicon oxide and why do we need high K gate dielectric to replace silicon oxide.


I believe the network of women in ITUT is very important to promote the more participation of women in standardization and in ITU, in ITUT specifically, and I would like it to see actually getting more women into leadership positions, so women in positions of being able to take decisions and to also structure the work in ITU and in ITUT.


TV star power couple Sofia Vergara and husband Joe Manganiello made waves in Beverly Park a few months back when they showed out $26 million on a gigantic estate here. More recently it's reported the couple had a trespasser lurking in their gated community claiming to know Sofia's son. They're safe, but let's hope this dude doesn't get past security again. If you're curious, today we're going to take a look at where they call home, like their new mega estate in Beverly Hills, the villa they've spent quarantine at, and more. Vergara is a Colombian-American actress, TV producer, comedian, model and more, who's reigned supreme as the best-paid actress on television, knocking out her competition with nearly 300 million in pre-tax earnings. These aren't only from her sitcom earnings, but also the result of high-paying endorsement deals and entrepreneurial ventures including a fashion line at Walmart and a furniture collab with Rooms2Go. In 2009, she began playing Gloria in the ABC comedy series Modern Family, which is by far her most well-known role and scored some movie roles over the years too. As of 2020, Sofia is also a judge on America's Got Talent. Her other half, Joe Manganiello, is an actor who began his film career playing Flash Thompson and Spider-Man but his breakout role was actually his werewolf in five seasons of HBO's True Blood series. He's also known for his movie roles especially in the Magic Mike franchises. When he's not flexing his muscles as a male dancer in those movies, Joe is also active with several charities, mainly the UPMC Children's Hospital of Pittsburgh, for which he serves on the board of trustees. So he's super fit and has a good heart. I can see why Sofia fell for him. The couple started dating in 2014 and tied the knot in a lavish ceremony in Florida in November 2015. In a recent interview for People, Joe explained, I knew pretty quickly that I could trust her and she knew pretty quickly that she could trust me. And now, the couple is buying 26 million dollar homes together. How sweet. At the time of this recording, Sofia has an estimated net worth of 180 million while Joe's at about 20 million so I'd say they're doing quite well. Hey guys it's Kara and today we're bringing you another house to our here and famous entertainment. We'll be taking a look at where famous couple Sofia Vergara and Joe Manganiello call home, like the new 26 million dollar spread they bought in Beverly Park, the other LA mansion they live at and more. So guys, can rate which one of their homes you like better down in the comments. If you like these videos make sure you're subscribed and ring that bell for notifications. We've also done house tours and links with Zoe Saldana and Arnold Schwarzenegger and we'll link to some at the end. Follow me over on Instagram to chat and as usual let me know who's house tour is next in those comments down below. Now let's get into this video. Before we get into their current homes, let's take a quick look at Joe's previous bachelor pad if you will. In 2015, around the time he got hitched to Sofia, Joe listed his Hollywood house on the market for just under 2 million. The mid-century modern home was located in the Hollywood Hills just a few short minutes from the sunset strip and had a large backyard ideal for entertaining. Joe purchased the house in 2013 for 1.8 million and it was nestled on a quarter acre of land in Nichols Canyon, a hillside area by Mulholland Drive. His neighbors here would have included the likes of Bruce Willis and Matthew McConaughey among other A-listers. The canyon has two waterfalls where water spills from the top of Hollywood Hills and trickles downstream. Joe's house spanned 2,500 square feet and featured two fireplaces, topped the line appliances in the updated kitchen and a two-car garage. There were four beds and four baths to accommodate the number of house guests. One to four and this is the living space right here. It has a wonderful modern open floor plan and this great contemporary large scale tile. This previous master suite, which overlooked the pool area, came with a walking closet, dressing area and ensuite bath with large rain shower and separate soaking tub. French doors in the living area open up to the yard where you'll find the beautiful comma-shaped pool complete with water feature and a hot tub.  I'll also turn around here so you can see all of these doors from the master, from the dining room that all open up to this incredible, incredible environment. Joe's home may have been the perfect bachelor pad, which is perhaps why he left it behind. Once he and Sophia joined forces, they definitely upgraded. Aside from the home that the successful couple recently snagged in the nicest neighborhood of all, Sophia continues to maintain her starter home in Beverly Hills just two miles down from the new mansion. She picked up this property in 2014 for $10.6 million prior to her marriage and has recently been living the quarantine lifestyle there. Which means we can consider this house Joe's now as well, you know, since they're married and all. Yes, that's Sophia and her niece posting a bit of a thirst trap on Instagram. The family also enjoyed a barbecue at the home the same day, which we can see in the less thirsty photos. Anyways, back to the house. Prior to this place, Sophia lived in a $1 million condo near Century City, L.A. So when she dropped 10 times that on this villa, you know it was a big deal. This Italian style mansion is celebrity size, spending 11,400 square feet with seven beds and 11 baths. It's fully gated and in an exclusive community, sitting on 0.6 of an acre parcel of land. A pair of cypress trees stand on either side of the front door that opens to the double height dramatic entry hall with a soaring ceilings and a floating iron staircase. There are common spaces like a fireplace, formal living room and formal dining room, both of which are placed next to a wood paneled study with exposed beam ceiling and built-in bookcases. Also on the main level of Sophia's home are one of the guest suites and the spacious chef's kitchen with breakfast nooks. Upstairs, her master suite is fit for a queen, while king and queen, I should say, considering there are separate his and hers baths and walking closets. Other features of this home include and are not limited to a third level with bar dedicated to entertaining, a 3,000 bottle wine cellar and wine tasting room, a movie theater, a card room, gym with sauna, two maids rooms and a large second floor terrace with fireplace. Although the yard isn't especially large, it is a covered dining space and a second dining area with built-in barbecue station, which I believe we saw in the pics. Of course, there was enough space left over for a swimming pool and spa as well. Not to mention, since moving in, Sophia decorated the mansion to suit her taste. Her home offers family style furniture and in other rooms, more elegant pieces. But one thing's for sure, it's full of design inspiration. She also loves entertaining and hosting friends and family, so she made sure her villa was perfect for that. Considering Sophia and Joe reportedly spent quarantine at this home rather than their new mansion, it's thought that the creepy trespassing incident occurred here as per TMZ. At least they can hide at the new mansion now. At the beginning of the summer, it was reported that the star couple snagged the former Beverly Hills mansion of baseball legend Barry Bonds. They beat a whopping $26 million to close on the European-style estate, and it's located in the much coveted, private, gated community of Beverly Park. If you watch this channel, you've heard of the neighborhood before because Beverly Park is awash with A-list celebrities and the most expensive homes you ever did see.  While the seven-dears-so homes in Beverly Park tend to rarely change hands, Sophia and Joe's mansion is familiar with the real estate rodeo, having no less than five owners in the past six years alone. But whatever way you spin it, the home is breathtaking. Originally built in 1999, the sprawling compound measures in at about 17,000 square feet, set on two acres of land and divided between the main house, a guest house and a poolside cabana. There's had to be six beds and about 10 baths here. Although listing photos aren't available for every room in the mansion, we can see the place is decked out in crystal chandeliers, silk carpeting and imported limestone, or at least it used to be.  Downstairs, there's a cavernous movie theater with gold accents and hand-stanceled ceilings. Can you say fancy? The couple's gated property has perfectly manicured grounds which were reportedly designed to embody the gardens of a palatial estate in Italy. There are emerald green lawns, olive trees and pathways leading past stone fountains. There's also a Grecian-inspired pool and surrounding patio while elsewhere the yard offers a sports court. All in all, Sophia and Joe got their hands on a property designed for some grand scale entertaining or just some indulgent family time. So I think I'll bring this house tour to an end here. After looking at power couple Sophia Vergara and Joe Manginello's homes including their two properties, both in Beverly Hills, what did you think? Be sure to rate the three houses in the comments down below or pick your fate. Honestly the two mansions they own at the moment kind of look the same to me. They're both Tuscan style villas and they're both absolutely massive. I also recently did a house tour of my place on my personal channel and while it's not even close to 17,000 square feet or even a fraction of that, I still like it. So we'll link you to my latest video and please like and subscribe to my personal channel. This is it, we're all moved in. Clearly it looks like I'm already living here. And this apartment didn't actually come with an island. The old kitchen did. So I had to order one but I'm absolutely in love with this one. Thanks for tuning in. Don't forget to follow me on Instagram and let me know whose house tour is up next. I'll see you all in the next one. Bye.


feel safe like I do. Till death do us part my name is Brandon Keith Avery and this is just my opinion. All right guys thank you very much for tuning in to my opinion slash review till death do us part before we begin to the review help me out by go ahead and click that subscribe button go ahead and click the bell so you can be notified and give me a thumbs up let's see if we can get this video to 100 likes. Now till death do us part just came out excuse my review for being extremely late this movie is being directed by Chris Stokes I'm like I know that name I know that name and I'm like yeah Chris Stokes he's like a music producer and has a lot to do in music and he's been directing a lot of films or not a lot of films but a few films and I guess he's just trying to dip his toe in this industry as well and just to be honest with you guys this is a extremely low budget production it is a B movie so I probably won't even rate it because I just don't feel like that's fair but this is a B movie and I actually did not know that going in because I never saw a trailer for this movie coming out I heard like I heard people talking about it I saw posters and you know maybe you heard a tv spot or something on the radio or in the background but I never actually sat down and watched the trailer I knew Tate Diggs was in the movie and somebody at work was like yeah you know Tate Diggs and I was like yeah I know I need to see that movie or whatever so I wanted to go see it you know not too long ago this I forgot what day it was but I just want to go see it and this is gonna be real quick and real short but like I said this is a really extremely low budget movie if you can make it past the first 30 minutes of this movie then you will be able to enjoy yourself for the most part because while there are a lot of bad things in this movie there are a lot of great good things and it does have some redeeming qualities and overall is an entertaining film that you may want to wait um I mean excuse me I'm so sorry you can go see it if you want to in the theaters to support but you know if you want to wait for this to come out on um you know DVD blu-ray red box you know there's nothing wrong with that but as far as the plot is concerned there is this couple by the name of Allison and Michael and they appear to have the perfect marriage but Michael has some serious dark issues with his past and insecurity issues and he becomes a controlling husband and his wife feels in danger and wants to get away we've seen this story played out in film and tv or whatever a thousand times and now they're just doing again in a cheaper version with a lot of black people and you know hey let's see how it was the acting from uh Allison her name is Jessica Vanessa de Leon at first 30 minutes it was trash it was atrocious it was cringe worthy to where you wanting to walk out of the movie well not the whole time but there was a one time where she was trying to moat anger and just show how frustrated she was with the marriage and you couldn't take her seriously it was laughable I was just kind of like oh my gosh like where did they get these people that I mean you know I can understand they may not have all the resources in the world but my goodness was this the first take because y'all did y'all not rehearse this this is horrible this is horrible um but yeah she's not having her marriage and she wants to escape or whatever and from that point it's pretty self-explanatory you think you have the whole movie figured out but aha there is a twist and there's a twist that I actually did not see coming and I really don't watch too many movies like this too much so they probably could have used this plot point before and a bunch of lifetime movies or something like that but it was a little twist in the movie that I did not see coming but that's pretty much what the movie is about for the most part is this couple they're not having the perfect marriage and Allison is wanting to get away and start another life with someone else but of course you know she has to stay away from her abusive husband and you know that's just kind of how the movie unfolds this kind of how things play out it was kind of frustrating like I do not under any circumstances you know feel that a man should hit a woman under no other than self-defense like a woman is trying to harm you you know uh with some brick or a knife or a gun at that point in time you have the right to your to defend yourself and even if a woman is slapping you or whatever even though I don't feel like a woman should slap a man I will walk away I'm just like I won't run away but I was like woman you need to get back you know walk away you know leave me alone all that I don't I don't condone violence I don't condone a man hitting a woman I bring it up I bring it up though even though I feel that way and this is gonna sound really sexist you know and just hear me out you always hear on tv shows like you know the man hits the woman and you hear this in the movie what she asked for or whatever she deserved it in this movie some of the things that the wife was doing I mean like why if it was just it was just stupid some of the things she was asking to get hit the man shouldn't have hit her but it's just like why would you say something like that to somebody that you know is abusive I mean you know they are not going to react well this way you're just pressing their buttons no the man still should not hit the woman so he still should not hit her okay but at the same time don't go and provoke somebody you know like if you know um you know if I know somebody doesn't like peanut butter well no that's that's just a bad example that that's a horrible example I was from the list but let me come up with a better example I mean well I don't like what you're doing so I'm just gonna stay over Becky's house because you're acting like I mean she did something like okay just leave and go over Becky's house why do you have to make an announcement why do you have the yelling in your face like is he gonna sit there and just be like okay honey you know have a good one bye you know call me when you get to bed he's not gonna do that just I mean why weren't you it it was just stupid and she was just kind of doing things like that throughout the movie that was very frustrating um but you know after all of that you know the relationship that she was having with Tay Diggs or whatever it was starting to progress it was starting to blossom it was starting to flourish and I really did like that and as you're watching the movie and you're knowing how and there's really just no stakes you know no one dies no one gets hurt no one does this no one gets that it's just a movie that runs itself to the end to where it's somewhat anticlimactic and you can pretty much predict after that twist you can pretty much predict what's going to happen in the movie um like I said I'm not going to rate this movie I don't even know because I just don't feel like this fair because this is a low budget B movie it is not up to the standards of a normal Hollywood production but I will say if I were to rate it that the movie is entertaining and it is worth a view if you want to go to a mat don't don't pay full price for this unless you're just trying to support Chris Stokes but you can do this in a matinee or you can wait for this to be read by us because even though all the bad it did have there was still some entertaining qualities about it that I did appreciate but guys that's just my opinion have you seen till death do us part do you want to see it how about turned you one how about turned you off do you agree with me or do you disagree with me let me know in the comment section below let's get this conversation going and keep flowing if you like this video go ahead and give me a thumbs up and if you don't like the video that's fine just leave me a comment below why and still give me a thumbs up since you're watching some youtube go here and subscribe to my youtube channel to get all the content that I have to provide you can click the bell and be notified when I make uploads you can also go to my website check me out there I do have written reviews you can bookmark it and also look me up on social media facebook instagram twitter all that good stuff is right there at the bottom of your screen and I made it very easy for you guys by providing a link to all that good stuff in the description box down below but guys I just want to thank you again for tuning into my opinion slash review starting take digs directed by Chris Stokes and before you go don't forget that my name is Brandon Keith Avery and that's just my opinion peace


I'm thinking about we're coming out of the stress. We got some big games coming up. That's really the only thing on my mind right now. Aaron, did it cross your mind in the ninth when you came up that you might get 60? You know, it's tough for a handed pitcher. Kelly throwing sidearm like that.  I just got to drive the guys in. You know, I'm so focused on just trying to get a good pitch ahead and, you know, get those runs in, I know we kind of had a big lead. I was just kind of back and forth. They were scoring their scrappy team. So, you know, I really wasn't thinking about a homer. I was just thinking about trying to do my job. When you see the ball being switched out, so does that get your attention? I didn't even notice. You know, I'm locked in on the pitcher, you know, situation, do my routine and go to work. You've mentioned before how you don't think about the numbers. How do you compartmentalize that when it comes to everybody's talking about it and when it comes to winning? How difficult is that?  You know, if I'm out in my focus to go out there and win a game, numbers, you know, that's just, there is numbers. You know, I'm focused on doing my can to be a good teammate, help the team win. If that means hitting a homer, then it means hitting a homer. But, you know, it's never, never been my focus, never been, you know, my main objective when I play this game. You're one point behind the value title right now.  How, like, good, pretty good average year in 5200 season. In your mind, how are you a better hitter now than last year or a year before? Stay on the field, get more at bats. That's all I'm back and sum it up. Sorry to even ask this before, but, like, growing up, did you ever dream of, like, make it a goal to win, like, a batting title or a triple crown, like, even growing up or dreaming about doing that in the minors or whatever? Like, is this, was that ever on your mind? I'm not trying to, like, take team goals aside, sorry. That's a good one, James. Man. Yeah, you set personal goals, especially as a 10-year-old kid, you have dreams and aspirations of what you want to try to accomplish in, you know, Major League Baseball if you ever get there.   Have you achieved any of those goals? Have you achieved any of those goals? Yeah, make it to the big leagues, being all-star, you know, the big one, you know, when a championship is still out there, but, you know, we got some time for that, we got a team that can do that, so hopefully we'll check that one off the list.


All right, so now we are going to take a quick look at how we are able to within tracker programs perform some data quality checks. So in a quick overview, we've just talked about the tracker to aggregate or actually vice yeah tracker to aggregate approaches. Let's talk about then some common issues with data quality in patient records. Then I'm going to do a really brief overview of how we can build out some program rules. These are some logic rules that you can put into tracker programs for data quality checks. This is not a tracker academy. We have academy specifically on building out tracker programs and goes into a lot of detail on how to build out program rules. I strongly encourage you to go to one of those to get a very detailed overview or skill set of how to do this. I'm going to give you some really basic overview just now. Then we're going to talk a little bit about how we can monitor completeness through program indicators. Again, I'm not going to go into detail on how to build program indicators. There's another academy for that, but I'm just going to give you a brief overview of how they can be applied for data quality. Let's talk about some common issues with data in tracker programs. Most of us are building very complex data entry forms in tracker. We're doing the same thing in aggregate as well. We're not building in a lot of workflow support to these forms. It's a problem in aggregate. It's even a bigger problem in tracker. If we're capturing individual patient data, we have got to put in a lot of workflow support. I'll give you an example of that. There's often a lot of questions. Can we calculate coverages based upon tracker data only? The general rule of thumb is probably not. You probably don't have the entire population coverage using just your tracker program. How do we then get calculate coverage based upon our tracker data or in combination of tracker and aggregate data? Then calculate completeness from individual patient records. Another tricky thing is when is a patient actually complete? All right. Let's first go into that workflow question. How do we address that with program rules? Well, it's actually easier for me to show you than to go through it. I'm going to give you a quick example. You're looking at a DHIS2 database for mass drug administration. What they are actually doing is they are using events to capture mass drug administration, so mass drug campaigns. I'm going to go into the capture app. This is not a demo database. This is actually a database from a real project. You're not able to go into this one. I'm just going to show it to you as an example. Then I'm going to go into an org unit. I'm going to select one of my programs. This is the capture app. This is a different app than data entry. I'm going to select one of my programs. Now this is an event program. I'm going to click new event. Now I'm opened a data entry form for an event program. In this case, it's a mass drug administration for LF. LF is lymphatic filariasis, which is a parasite drug. If you're doing MDAs, mass drug administration, for this particular disease. What I'm going to start doing is putting in some data here, just to show you some of the warnings that pop up as I'm putting this data in. I can just put in a contract to the implementation total persons. Tim, here's the first example of workflow support. In programs, I can put in boxes or I can put in questions that if I answer will prompt additional questions. Again, workflow support. We don't want to build in as much workflow support. As you're filling out questions, you want to be able to hide certain answers or you want to be able to show other questions based upon a certain answer. In this box, I have a tick box that says yes, and if I do that, then it opens another field that I need to fill in. I'm just going to start putting in more information here. This next question is a good one. We say year started. Here, I should be putting in a year value. I'm going to put in a number that's not a year and look what happens. I put in like 200,000. We're not even close to the year 200,000 yet. It says invalid year. This year must be between 1990 and 2020. If I go to 2000, see the warning goes away. This is a great example of a simple program rule. If I put in a number that doesn't make sense, it's checking the value that I'm putting in. It's telling me in real time if this is an appropriate number. I'm going to put in just a couple more. I'm just putting in random numbers right now. You can see I've triggered another data quality check. In this case, what is it saying? Well, I've actually got an error box in the top right corner here. It says persons targeted cannot be greater than the population eligible for PC. PC is a treatment process. Here, they say, okay, look, I've got 10 here, and then it says person target cannot be greater than the population. Then it's telling me, okay, check here, check here, check here, and great. I have 10 here, but I have 10 there, but this isn't saying it's not right. If I just put in 10, or zero, and zero, great, now it goes away. The sum of these could not be greater than this. That's what it was. When I put in the zeros, it went away. All right, now I've got another data quality check. I just put in some more random numbers and saying the sum of male plus female treated cannot be greater than the sum of pre-sac, sac, and adults targeted. These are aged breakdowns, and it's saying the sum of male and female treated cannot be greater than these values here. What did I do? I put in 10 male plus 10 female, that's 20, and then the sum of this is just 10. Let's try to fix that. I put in zero here, great, now that went away. I just put in some more random numbers, and guess what? I got two more errors saying the sum of male and female, this male should be equal to the sum of treated. Okay, so they're saying that this sum male female should also be equal to the sum of these three, and then it's saying also the sum of pre-sac, sac, treated cannot be greater than the sum of pre-sac targeted. So they're saying this sum cannot be greater than this sum. So let me go in and fix that. All right, so now all my data quality checks went away. So I just wanted to give you a quick demonstration of kind of program rules in action. So this is a real project. They're entering data across dozens of countries actually in this particular program. Hundreds of people are entering data using this particular reporting form, and you can see all of the various data quality checks are just an example of some of the data quality checks that are coming up as they're putting the data in. You can also see some of the workflow support. So for example, I ask a question if I say yes, then it gives me another box to fill out. These are done via program rules. So let me get back to my PowerPoint presentation. All those different data quality checks that you just seen are done via program rules in DHIS too. So essentially what is a program rule? A program rule allows you to put in dynamic behaviors in response to user input. So just as you saw, I was ticking boxes and more things were coming up or I was putting in values and it was causing alerts and error warnings. You know, that's the kind of dynamic behaviors we're talking about. It allows you to send a lot of direct user feedback. So as you're putting stuff in, you're telling the user, no, that doesn't make sense or you need to check this or that those numbers can't add up. And you are building these out as expressions. And we will talk a little bit about the expression builder just in a second here. But essentially, these expression builders have a lot of different options for you. It's not just about showing warnings or errors or tick boxes. What I showed you, there's lots of things that this can do. And again, there's a whole academy that goes over this. But just to make the point that you can really tailor these messages, these warnings, this user feedback, this workflow support to using a lot of different ways, a lot of different features. So most actions using program rules will happen immediately when the data values are entered. So that's exactly what you saw me doing. So as I was putting the values in, there was something happening immediately. And that program rule, in terms of how it's configured, it constitutes three things, variables, expressions, and actions. I'm going to go through these very quickly. And again, to get more detailed answers and explanations, we will have to point you to another academy, or certainly you can ask questions, and we have lots of resources for this. So what are program variables? Essentially, program variables are represent attributes and data element values, which can be evaluated as part of an expression. So essentially, these are the data elements and attributes that you actually define. So as part, usually they're data elements, sometimes they're attributes. And you are, once you make them as a variable, you can use them in multiple program rules. These can include data values and attributes and operate these as expressions. Let me kind of skip through that. So here's the screen. If you want to make a program rule variable, this is really the first step. You have to define the variables, then you can put them into your expressions. And then from your expressions, you define the actions. So in this case, we're looking at a program variable called sex. And the sex is a tracked entity attribute. So this is like if you have a patient and the patient is male or female, then this would be sex that they're using here. The expression is the next star. So once you've defined your program rule variables, you have to define your expressions.  So I'll skip through that. In the expression, you have to build out operators. Again, we don't have time to go over this in a lot of detail. There are clear documentation linked here in the PowerPoint, providing you what these are. You have to do a little bit of programming. And it's not programming in like you have to know JavaScript or HTML or something like that. But there's just syntax in which you have to build out these program rule expressions in. And you can see here, I won't go over each one of these examples, but you can see that they can become actually very complex. There's a lot of flexibility here on how what you can actually do with the program rule expressions. So you can say like, if statements are greater than statements or statements and statements, you can really, really build some complicated things here. Of course, it's better to keep stuff simple. But you can say like, if they enter this, they enter sex as male, if they enter age is greater than 35, if they enter BMI as this, then recommend this action. You can really base it, these program rules are based upon several multiple variables and give specific feedback as the data is coming in. Not useful for just data quality, also useful for just workflow support. Custom functions, I'm not going to go over this, but suffice to say that you can build out various custom functions. So you can count the number of days in a month. You can count the number of days between events. You can calculate, you can prompt actions if there's a specific value. We call these our D2 functions. You can see that on the left side of the column there. That's just an example. All right, so actions. So we define our variables, we define our expression, and then we can define many different actions that have an example that I showed you. The action that I was triggering was show warnings and show an error. So you saw that as I entered values and there was data quality checks happening, it was popping up different errors saying these values don't sum up together. You need to double check this. There are many different actions that could happen. So I was just showing you basically one action. You can check the docs here. If you click on this actions icon, it'll take you to the docs and all the various actions. There's really a lot of different choices here. So this is a brief summary. So you enter the program rule details, then you enter your program rule expression, and then you define your actions. The other thing that I want to point out is that many of you are using Android, the mobile app, to be able to capture tracker data as well now. So you're not just using your PCs, you actually have folks in the field, community health workers, outreach workers, clinicians that are using the application. The point to be made about folks that are using the application is as they're filling out data, make it as easy as possible. Put in pictures, make different colors, make it so that each one of the answers to each one of the questions is clear and distinct. So here you see an example of a malaria program where a community health worker is filling out malaria cases. And you can see that for the answers in the top right corner, they're saying, well, what is, how did you diagnose this malaria test? Do you use microscopy and RDT or there was no test at all? And you can see that there's pictures. There are clearly, there's clear colors. It's very simple even for someone who may be illiterate to answer this question, right? Which one did you use? And again, this is the best practice. The best practice is make it as clear and simple as possible for folks to be able to enter this data. The next question of course is, or a later question is, what are the treatment types? And again, very clear pictures, different colors. And so they would be able to distinguish which treatment type they provided. So program indicators are another way in which we're able to calculate values based upon tracker data. Many of you may be wondering what's different between a standard indicator and a program indicator. The main difference is that a program indicator can have a little bit more logic put into it and it calculates values based within a single program. Whereas a standard indicator cannot support much logic, but it can calculate values across multiple programs or multiple data sets. Okay, so a standard indicator across multiple programs or data sets, a program indicator, you can export more expressions, more logic to it, but it can only calculate within a single program. So here's an example of maybe a program indicators that look at completeness of data that's entered in tracker programs. Two examples here, the first one is count of BCG doses given divided by total number of tracked children. So what is that giving us? That's giving us basically a coverage of BCG doses across all of the children that we have captured data against in our tracker program. A very simple program indicator may be applicable to like an immunization program. And the second example here is like return the percentage of tracked children who have recoded a BCG dose. So tell me the percentage of children that have received a BCG dose. And so this is very simple examples of how we're able to actually look at something like BCG dose coverage within a or completeness within a tracker program. So the question becomes a lot of times is if we have tracker data and we have aggregate data, how do we combine those into a single coverage indicator? What we typically recommend is that you do not use tracker data as your sole denominator for coverage indicators. Why is that? Because typically most tracker programs do not have national coverage, meaning that most of them are still in some stage of scaling or pilot. So maybe you have a couple of districts using tracker and they are capturing individual treatment or service data through tracker. But then you have other districts that are maybe not using tracker or they're not using it as well. And they're still capturing that aggregated data on a monthly basis or something.  Well you need to potentially combine the two sources of data together into a standard indicator. But we don't recommend that you necessarily solely use tracker data unless you know that you have 100% coverage of your tracker program across the entire country. And let me just kind of skip to the point here. So here we have kind of what's the good practice? The best practice is to have two different indicators potentially. So you have your national coverage that's using your aggregate denominator. And then you have another coverage that's using tracker data. And you're able to see those side by side. And so that's what we're actually able to look at here. We have the coverage indicators on the left that are coming from the tracker program. And then we have the individual data that's coming from the aggregate side. So you can see that in the green is the tracker coverage. The blue is the national coverage coming from the aggregate. So you're able to see those side by side. And then the same thing on the right side, the green is the total number of immunized children. And the blue is coming from your aggregate data. And the blue is the total number of children registered in the immunization program. So you're able to see these side by side. Again, the point being that as it stands right now, the general recommendation is you put your tracker data alongside your aggregate data and view these as somewhat distinct unless you know that you have 100% coverage of your tracker program, which is quite rare. All right, just one other slide here. Sorry. Here we want to talk about how you monitor aggregate data compiled from a tracker program to ensure that errors have not occurred in the data transfer. Essentially, what we're looking for is a one-to-one relationship. So what we are saying is that Yuri just talked through some of the tools that can be used for this. But you also want to put in some some program indicators to make sure that the values that are being transferred. So for example, if I am transferring this month's aggregate or tracker data into aggregate, let's make a program indicator that sums up all of the tracker data for that data element for that month. And then let's see that program indicator. And then when we when we push the data from tracker to aggregate, let's make sure that data element that we see in our aggregate is the same as that program indicator. So we can do a check this. So we make sure that we kind of have a one-to-one relationship. Sometimes it can become quite problematic with the transfer process. If you don't have these data quality checks kind of in place to monitor just the transfer, did everything actually go through correctly? Okay. I think I'm going to skip the last slide. Actually, Yuri covered it quite thoroughly, I think. And so that brings us time now. It's 12.06 Oslo time. So we are going to go ahead and take our break. Well, let's see if there's any questions. Nora, any questions from the community? Martin? Scott, you answered the questions about Excel into tracker. And then there's a question from Derivi again. And if we calculate coverage, if we use event program to do surveys of representative samples, I'm using it for our annual household survey to calculate coverage. I'm not sure. And there's also another question from Rose. So can we use, can we calculate coverage if we're using event data to do surveys of representative samples? Yeah, of course you can. What we were talking about, and I think that seems like maybe a lower level, like a DHS, like a demographic household survey or something like that, you certainly can do that. And that's the methodology that DHS also employs. What we are saying is that maybe for like coverage indicators that are like total immunized or percentage of immunized children or something like that, fully immunized children, typically we don't have tracker programs yet that are at national scale to be able to calculate that one with 100% certainty from just tracker data. But I think that if you're looking at coverage calculations that are just from like maybe survey data and you're using event capture to get that survey data, then I think that's probably fine. It's not so much associated with like clinical service delivery. Then Rose has a question here, would it be possible to do some program notifications and tracker events as bulk collective summary, not a single notification? Yes, I think so. Oh, you want to schedule notifications from tracker. I believe so. So you can also have something called program notifications. That's when you're actually building the program that you can apply. And I think maybe not using a program rule, but a program notification might be the answer to that. But to be honest with you Rose, I am not the world's expert on tracker. And so what we need to do is I need to pass your question along to Yuri, who would have a better answer than me, I hope. All right. So now we're at 1208. If you were wondering if I could share with you access to the database and just a quick demo, I cannot. That is just a, that is a production since developed by a DHIS2 implementer that I asked if I could just do a quick demo of their data capture forms and they said that was fine, but it's not, it's not publicly available instance. Okay. So let us take our 15 minute break and we will be then coming back now at, I guess it would be 1225. So, Nora, would that give us sufficient time to go through your session after the break? Yes, Scott, that's fine. We've just had a request for the uploading of the PowerPoint presentations, please. Right. I believe mine is already uploaded, but we need to grab Yuri's as well. Yes. Then 1225, we will meet again. Okay. Great. We'll see you back here in 15 minutes at 1225 Oslo time.