Complementary memtransistors for neuromorphic computing: How, what and why

Abstract

Abstract Memtransistors in which the source−drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic computing. On the other side, it is known that the complementary metal-oxide-semiconductor (CMOS) field effect transistors have played the fundamental role in the modern integrated circuit technology. Therefore, will complementary memtransistors (CMT) also play such a role in the future neuromorphic circuits and chips? In this review, various types of materials and physical mechanisms for constructing CMT (how) are inspected with their merits and need-to-address challenges discussed. Then the unique properties (what) and potential applications of CMT in different learning algorithms/scenarios of spiking neural networks (why) are reviewed, including supervised rule, reinforcement one, dynamic vision with in-sensor computing, etc. Through exploiting the complementary structure-related novel functions, significant reduction of hardware consuming, enhancement of energy/efficiency ratio and other advantages have been gained, illustrating the alluring prospect of design technology co-optimization (DTCO) of CMT towards neuromorphic computing.