Vertical Metal-Oxide Electrochemical Memory for High-Density Synaptic Array Based High-Performance Neuromorphic Computing

Abstract

Cross-point arrays of analog synaptic devices are expected to realize neuromorphic computing hardware for neural network computations with compelling speed boost and superior energy efficiency, as opposed to the conventional hardware based on the von Neumann architecture. To achieve desired characteristics of analog synaptic devices for fully parallel vector-matrix multiplication and vector-vector outer-product updates, metal-oxide based electrochemical random-access memory (ECRAM) is proposed as a promising synaptic device due to its complementary metal-oxide-semiconductor-compatibility and outstanding synaptic characteristics over other non-volatile memory candidates. In this work, ECRAM devices with 3D vertical structure is fabricated to demonstrate a minimal 4F(2) cell size, highly scalable channel volume and low programming energy, providing optimized synaptic device performance and characteristics as well as high integrity as a cross-point array. Various weight-update profiles of the vertical ECRAM devices are obtained by adjusting programming voltage pulses, exhibiting trade-offs among dynamic range, linearity, symmetry, and update deviation. Based on simulation with advanced algorithms for analog cross-point array and neural network designs, the potential of vertical ECRAM for high-density array is evaluated. Simulation studies suggest that the neuromorphic computing performance can be improved further by balancing the weight update characteristics of vertical ECRAM.