Artificial optoelectronic synapses with both electrical and light‐induced synaptic behaviors have recently been studied for applications in neuromorphic computing and artificial vision systems. However, the combination of visual perception and high‐performance information processing capabilities still faces challenges. In this work, the authors demonstrate a memristor based on 2D bismuth oxyiodide (BiOI) nanosheets that can exhibit bipolar resistive switching (RS) performance as well as electrical and light‐induced synaptic plasticity eminently suitable for low‐power optoelectronic synapses. The fabricated memristor exhibits high‐performance RS behaviors with a high ON/OFF ratio up to 105, an ultralow SET voltage of ≈0.05 V which is one order of magnitude lower than that of most reported memristors based on 2D materials, and low power consumption. Furthermore, the memristor demonstrates not only electrical voltage‐driven long‐term potentiation, depression plasticity, and paired‐pulse facilitation, but also light‐induced short‐ and long‐term plasticity. Moreover, the photonic synapse can be used to simulate the “learning experience” behaviors of human brain. Consequently, not only the memristor based on BiOI nanosheets shows ultra‐low SET voltage and low‐power consumption, but also the optoelectronic synapse provides new material and strategy to construct low‐power retina‐like vision sensors with functions of perceiving and processing information.

The memristor is an excellent candidate for nonvolatile memory and neuromorphic computing. Recently, two-dimensional (2D) materials have been developed for use in memristors with high-performance resistive switching characteristics, such as high on/off ratios, low SET/RESET voltages, good retention and endurance, fast switching speed, and low power and energy consumption. Low-power memristors are highly desired for recent fast-speed and energy-efficient artificial neuromorphic networks. This Perspective focuses on the recent progress of low-power memristors based on 2D materials, providing a condensed overview of relevant developments in memristive performance, physical mechanism, material modification, and device assembly as well as potential applications. The detailed research status of memristors has been reviewed based on different 2D materials from insulating hexagonal boron nitride, semiconducting transition metal dichalcogenides, to some newly developed 2D materials. Furthermore, a brief summary introducing the perspectives and challenges is included, with the aim of providing an insightful guide for this research field.

Memristors based on CVD-grown 2D layered MoSe2 nanosheets show potential applications in artificial synapses and nociceptors for neuromorphic computing.

The neuromorphic system is an attractive platform for next-generation computing with low power and fast speed to emulate knowledge-based learning. Here, we design ferroelectrictuned synaptic transistors by integrating 2D black phosphorus (BP) with a flexible ferroelectric copolymer poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)). Through nonvolatile ferroelectric polarization, the P(VDF-TrFE)/BP synaptic transistors show a high mobility value of 900 cm 2 V −1 s −1 with a 10 3 on/ off current ratio and can operate with low energy consumption down to the femtojoule level (∼40 fJ). Reliable and programmable synaptic behaviors have been demonstrated, including paired-pulse facilitation, long-term depression, and potentiation. The biological memory consolidation process is emulated through ferroelectric gate-sensitive neuromorphic behaviors. Inspiringly, the artificial neural network is simulated for handwritten digit recognition, achieving a high recognition accuracy of 93.6%. These findings highlight the prospects of 2D ferroelectric field-effect transistors as ideal building blocks for high-performance neuromorphic networks.

The WSe2-based memristor demonstrates the controllable digital and analog resistive switching behavior. Moreover, it can be used to emulate the “learning-forgetting-relearning” experience and performs image recognition with high recognition accuracy.