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

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

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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
JournalScientific Reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

Fingerprint

Dive into the research topics of 'Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing'. Together they form a unique fingerprint.

Cite this