Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing

Srikrishna Sagar, Kannan Udaya Mohanan, Seongjae Cho, Leszek Majewski, Bikas C. Das

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Abstract

Here, various synaptic functions and neural network simulation based pattern-recognition using novel, solution-processed organic memtransistors (memTs) with an unconventional redox-gating mechanism are demonstrated. Our synaptic memT device using conjugated polymer thin-film and redox-active solid electrolyte as the gate dielectric can be routinely operated at gate voltages (VGS) below − 1.5 V, subthreshold-swings (S) smaller than 120 mV/dec, and ON/OFF current ratio larger than 108. Large hysteresis in transfer curves depicts the signature of non-volatile resistive switching (RS) property with ON/OFF ratio as high as 105. In addition, our memT device also shows many synaptic functions, including the availability of many conducting-states (> 500) that are used for efficient pattern recognition using the simplest neural network simulation model with training and test accuracy higher than 90%. Overall, the presented approach opens a new and promising way to fabricate high-performance artificial synapses and their arrays for the implementation of hardware-oriented neural network.
Original languageEnglish
Article number3808
Pages (from-to)1-12
Number of pages12
JournalScientific Reports
DOIs
Publication statusPublished - 9 Mar 2022

Keywords

  • Organic transistor
  • Artificial synapse
  • Neuromorphic computing

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