Mimicking Biological Synaptic Functionality with an Indium Phosphide Synaptic Device on Silicon for Scalable Neuromorphic Computing

Neuromorphic or “brain-like” computation is a leading candidate for efficient, fault-tolerant processing of large-scale data as well as real-time sensing and transduction of complex multivariate systems and networks such as self-driving vehicles or Internet of Things applications. In biology, the synapse serves as an active memory unit in the neural system and is the component responsible for learning and memory. Electronically emulating this element via a compact, scalable technology which can be integrated in a three-dimensional (3-D) architecture is critical for future implementations of neuromorphic processors. However, present day 3-D transistor implementations of synapses are typically based on low-mobility semiconductor channels or technologies that are not scalable. Here, we demonstrate a crystalline indium phosphide (InP)-based artificial synapse for spiking neural networks that exhibits elasticity, short-term plasticity, long-term plasticity, metaplasticity, and spike timing-dependent plasticity...