Inherent Stochastic Learning in CMOS-Integrated HfO2 Arrays for Neuromorphic Computing

Abstract

Based on the inherent stochasticity of CMOS-integrated HfO2-based resistive random access memory (RRAM) devices, a new learning algorithm for neuro-morphic systems is presented. For this purpose, the device-to-device variability of CMOS-integrated 4-kbit 1T-1R arrays is examined. To demonstrate the performance of the stochastic learning algorithm and the potential of RRAM technologies for neuro-morphic systems, a two-layer mixed-signal neural circuit for pattern recognition is implemented and tested with MNIST data.