Low-Voltage Electrochemical Lixwo3 Synapses With Temporal Dynamics For Spiking Neural Networks

Neuromorphic computing has the great potential to enable faster and more energy-efficient computing by overcoming the von Neumann bottleneck. However, most emerging nonvolatile memory (NVM)-based artificial synapses suffer from insufficient precision, nonlinear synaptic weight update, high write voltage, and high switching latency. Moreover, the spatiotemporal dynamics, an important temporal component for cognitive computing in spiking neural networks (SNNs), are hard to generate with existing complementary metal-oxide-semiconductor (CMOS) devices or emerging NVM. Herein, a three-terminal, LixWO3-based electrochemical synapse (LiWES) is developed with low programming voltage (0.2 V), fast programming speed (500 ns), and high precision (1024 states) that is ideal for artificial neural networks applications. Time-dependent synaptic functions such as paired-pulse facilitation (PPF) and temporal filtering that are critical for SNNs are also demonstrated. In addition, by leveraging the spike-encoded timing information extracted from the short-term plasticity (STP) behavior in the LiWES, an SNNs model is built to benchmark the pattern classification performance of the LiWES, and the result indicates a large boost in classification performance (up to 128x), compared with those NO-STP synapses.