WO_x-Based Synapse Device With Excellent Conductance Uniformity for Hardware Neural Networks

Abstract

Hardware neural networks (HNNs) which use synapse device (SD) arrays show promise as an approach to energy efficient parallel computation of massive vector-matrix multiplication. To maximize the inference accuracy of application-specific HNNs, we propose a highly reliable 2-terminal SD with fixed resistance based on WOx films. First, we investigate the device requirements of an array-based HNN through MATLAB and SPICE simulations taking into account the parasitic resistance effects in the array. On top of that, to fabricate the SD we utilize the intrinsic properties of the WOx film, which exhibits substantial changes in conductivity from 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-8</sup> to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> Ω <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> by varying the oxygen vacancy concentration. After the process optimization of oxide stoichiometry and electrode materials, we can form nanoscale WO×-based SDs with excellent conductance uniformity and I-V linearity. Our results show that inference accuracy is significantly improved by using WOx-based SD arrays even in advanced node scaling. Through experimental hardware implementation, 16 × 16 pixel images are correctly classified and we show the potential of WOx-based SD for future large-scale HNN applications.