Stuck-at Faults in ReRAM Neuromorphic Circuit Array and their Correction
  through Machine Learning
Vedant Sawal Hiu Yung Wong
In this paper, we study the inference accuracy of the Resistive Random Access
Memory (ReRAM) neuromorphic circuit due to stuck-at faults (stuck-on,
stuck-off, and stuck at a certain resistive value). A simulation framework
using Python is used to perform supervised machine learning (neural network
with 3 hidden layers, 1 input layer, and 1 output layer) of handwritten digits
and construct a corresponding fully analog neuromorphic circuit (4 synaptic
arrays) simulated by Spectre. A generic 45nm Process Development Kit (PDK) was
used. We study the difference in the inference accuracy degradation due to
stuck-on and stuck-off defects. Various defect patterns are studied including
circular, ring, row, column, and circular-complement defects. It is found that
stuck-on and stuck-off defects have a similar effect on inference accuracy.
However, it is also found that if there is a spatial defect variation across
the columns, the inference accuracy may be degraded significantly. We also
propose a machine learning (ML) strategy to recover the inference accuracy
degradation due to stuck-at faults. The inference accuracy is improved from 48%
to 85% in a defective neuromorphic circuit.