All‐solid‐state synaptic transistor with ultralow conductance for neuromorphic computing – yang – 2018 – advanced functional materials – wiley online library

Electronic synaptic products are important foundations for neuromorphic computational systems that may exceed the restrictions of von Neumann architecture. Although two‐terminal memristive products are shown to become possible candidates, they are afflicted by several shortcomings associated with the filament formation mechanism including nonlinear switching, write noise, and device conductance, which limit the precision and efficiency. Electrochemical three‐terminal transistors, where the funnel conductance could be tuned without filament formation offer an alternative platform for synaptic electronics. Here, an all‐solid‐state electrochemical transistor created using Li ion–based solid dielectric and 2D α‐phase molybdenum oxide (α‐MoO3) nanosheets because the funnel is shown. These units achieve nonvolatile conductance modulation within 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 transistor with ultralow conductance for neuromorphic computing - yang - 2018 - advanced functional materials - wiley online library conductance modulation

Resourse: https://onlinelibrary.wiley.com/doi/10.1002/

All‐solid‐state synaptic transistor with ultralow conductance for neuromorphic computing - yang - 2018 - advanced functional materials - wiley online library mechanism including nonlinear switching

Neuro-Inspired Computing with Synaptic and Neuronal Devices – Shimeng Yu