Dot-product computation and logistic regression with 2D hexagonal-boron nitride (h-BN) memristor arrays

Abstract

This work reports on the hardware implementation of analog dot-product operation on arrays of two-dimensional (2D) hexagonal boron nitride (h-BN) memristors. This extends beyond previous work that studied isolated device characteristics towards the application of analog neural network accelerators based on 2D memristor arrays. The wafer-level fabrication of the memristor arrays is enabled by large-area transfer of CVD-grown few-layer (8 layers) h-BN films. Individual devices achieve an on/off ratio of >10, low voltage operation (∼0.5 V set/V reset), good endurance (>6000 programming steps), and good retention (>104 s). The dot-product operation shows excellent linearity and repeatability, with low read energy consumption (∼200 aJ to 20 fJ per operation), with minimal error and deviation over various measurement cycles. Moreover, we present the implementation of a stochastic logistic regression algorithm in 2D h-BN memristor hardware for the classification of noisy images. The promising resistive switching characteristics, performance of dot-product computation, and successful demonstration of logistic regression in h-BN memristors signify an important step towards the integration of 2D materials for next-generation neuromorphic computing systems.