Self-Selective Multi-Terminal Memtransistor Crossbar Array for In-Memory Computing

Two-terminal resistive switching devices are commonly plagued with longstanding scientific issues including interdevice variability and sneak current that lead to computational errors and high-power consumption. This necessitates the integration of a separate selector in a one-transistor-one-RRAM (1T-1R) configuration to mitigate crosstalk issue, which compromises circuit footprint. Here, we demonstrate a multi-terminal memtransistor crossbar array with increased parallelism in programming via independent gate control, which allows in situ computation at a dense cell size of 3–4.5 F2 and a minimal sneak current of 0.1 nA. Moreover, a low switching energy of 20 fJ/bit is achieved at a voltage of merely 0.42 V. The architecture is capable of performing multiply-and-accumulate operation, a core computing task for pattern classification. A high MNIST recognition accuracy of 96.87% is simulated owing to the linear synaptic plasticity. Such computing paradigm is deemed revolutionary toward enabling data-centric applications in artificial intelligence and Internet-of-things.

Authors

Xuewei Feng; Sifan Li; Swee Liang Wong; Shi Wun Tong; Li Chen; Panpan Zhang; Lingfei Wang; Xuanyao Fong; Dongzhi Chi; Kah‐Wee Ang

Published

Jan 14, 2021

Journal or venue

ACS Nano

DOI

10.1021/acsnano.0c09441

Paper type

journal article

Source

Openalex