Tunable optoelectronic memristor based on MoS2/BaTiO3 for neuromorphic vision

Human vision inspired neuromorphic devices have integrated architectures that combine sensing, computing, and storage functions, which can fundamentally avoid the energy waste caused by frequent data movement in the currently widely used von Neumann architecture, and have crucial application potential in advanced artificial intelligence chips that pursue low power consumption and low latency. However, previously reported visual neuromorphic devices either suffer complex floating gate, vertically stacked multilayer structures, or necessitate separated optical-sensing and synaptic units, realizing highly compact, non-volatile optoelectronic response and continuously tunable conductivity within a sententious architecture remains a significant challenge. Here, we present a low-cost exfoliation and transfer method combined with spin-coating to fabricate molybdenum disulfide (MoS 2 ) / barium titanate (BaTiO 3 ) heterostructured optoelectronic devices. Based on the ferroelectricity of BaTiO 3 and the charge transport characteristics of MoS 2 , the hysteresis of ferroelectric polarization upon both electric and optical stimulation successfully endow reliable resistance state switching abilities, showing the advantages of low bias voltage operation (±2 V) and distinct 16 conductance states under light pulse irradiation. Besides, the MoS 2 / BaTiO 3 device can be further used to emulate biological synaptic behavior and accomplished the transition from short-term memory (STM) to long-term memory (LTM). Notably, leveraging the dual characteristics of imaging and neuromorphic behavior, we construct a multi-layer perceptron network integrating visual perception and image recognition, showing an accuracy of 97.6% in the Modified National Institute of Standards and Technology (MNIST) pattern recognition task. This work introduces a simple MoS 2 / BaTiO 3 heterojunction architecture device, offering integrated perception, storage, and computing capabilities, providing a new possibility for future compact neuromorphic computing devices.