  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.
