All‐Solid‐State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing - Yang, Chuan‐Sen; Shang, Da‐Shan; Liu, Nan; Fuller, Elliot J.; Agrawal, Sapan; Talin, A. Alec; Li, Yong‐Qing; Shen, Bao‐Gen; Sun, Young (Advanced functional materials, 2018)
Electronic synaptic devices are important building blocks for neuromorphic computational systems that can go beyond the constraints of von Neumann architecture. Although two‐terminal memristive devices are demonstrated to be possible candidates, they suffer from several shortcomings related to the filament formation mechanism including nonlinear switching, write noise, and high device conductance, all of which limit the accuracy and energy efficiency. Electrochemical three‐terminal transistors, in which the channel conductance can be tuned without filament formation provide an alternative platform for synaptic electronics. Here, an all‐solid‐state electrochemical transistor made with Li ion–based solid dielectric and 2D α‐phase molybdenum oxide (α‐MoO3) nanosheets as the channel is demonstrated. These devices achieve nonvolatile conductance modulation in an ultralow conductance regime (<75 nS) by reversible intercalation of Li ions into the α‐MoO3 lattice. Based on this operating mechanism, the essential functionalities of synapses, such as short‐ and long‐term synaptic plasticity and bidirectional near‐linear analog weight update are demonstrated. Simulations using the handwritten digit data sets demonstrate high recognition accuracy (94.1%) of the synaptic transistor arrays. These results provide an insight into the application of 2D oxides for large‐scale, energy‐efficient neuromorphic computing networks. All‐solid‐state synaptic transistors based on 2D α‐MoO3 nanosheets are fabricated. The operation mechanism is based on the gate voltage–induced reversible intercalation of Li‐ion dopants into α‐MoO3 channel lattice, which engenders bidirectional, near‐linear analog modulation of channel conductance in an ultralow conductance regime (<75 nS). The essential functionalities of synapses and neuromorphic computing for image recognition are demonstrated.