  Synapse is a key element of any neuromorphic computing system which is mostly
constructed with memristor devices. A memristor is a two-terminal analog memory
device. Memristive synapse suffers from various challenges such as forming at
high voltage, SET, RESET failure, and READ margin or resolution issue between
two weights. Enhanced READ resolution is very important to make a memristive
synapse functionally reliable. Usually, the READ resolution is very small for a
memristive synapse with 4-bit data precision. This work considers a
step-by-step analysis to enhance the READ current resolution for a
current-controlled memristor-based synapse. An empirical model is used to
characterize the HfO2-based memristive device. 1st and 2nd stage device of our
proposed synapse can be scaled to enhance the READ current margin up to ~ 4.3x
and ~ 21% respectively. Moreover, READ current resolution can be enhanced with
run-time adaptation features such as READ voltage scaling and body biasing. The
READ voltage scaling and body biasing can improve the READ current resolution
by about 46% and 15% respectively. TENNLabs' neuromorphic computing framework
is leveraged to evaluate the effect of READ current resolution on
classification applications. Higher READ current resolution shows better
accuracy than lower resolution with different percentages of read noise
scenarios.
