Learning to Design Analog Circuits to Meet Threshold Specifications
Dmitrii Krylov Pooya Khajeh Junhan Ouyang Thomas Reeves Tongkai Liu Hiba Ajmal Hamidreza Aghasi Roy Fox
Automated design of analog and radio-frequency circuits using supervised or
reinforcement learning from simulation data has recently been studied as an
alternative to manual expert design. It is straightforward for a design agent
to learn an inverse function from desired performance metrics to circuit
parameters. However, it is more common for a user to have threshold performance
criteria rather than an exact target vector of feasible performance measures.
In this work, we propose a method for generating from simulation data a dataset
on which a system can be trained via supervised learning to design circuits to
meet threshold specifications. We moreover perform the to-date most extensive
evaluation of automated analog circuit design, including experimenting in a
significantly more diverse set of circuits than in prior work, covering linear,
nonlinear, and autonomous circuit configurations, and show that our method
consistently reaches success rate better than 90% at 5% error margin, while
also improving data efficiency by upward of an order of magnitude. A demo of
this system is available at circuits.streamlit.app