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.
Highlights
Various synaptic functions and neural network simulation based pattern-recognition using novel, solution-processed organic memtransistors with an unconventional redox-gating mechanism are demonstrated
It is shown that the availability of large number of discrete conducting states programmed by voltage pulses applied to the gate and improved transistor performance make memTs very promising devices to be used as artificial synapses that consume as little energy as 250 pJ per synaptic action (SA)
resistive switching (RS) property of 3-terminal devices is demonstrated by the channel conductivity switching from a low (OFF) to a high (ON) state by applying a suitable gate voltage (VGS)[26,31]
Summary
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. The brain can efficiently process real-time unstructured signals such as sound, light, pressure, heat, and taste, which is impossible to realise using today’s computers To perform such complex tasks, the brain functions in massively parallel ways using interconnected neurons and synapses. Even though these devices consume much less energy compared with the software-driven neural network hardwares, they still cannot achieve the energy efficiency close to the brain. High-performance, low-voltage redox-gated organic memtransistors (memTs) which efficiently emulate various synaptic actions of a brain are demonstrated. It is shown that the availability of large number of discrete conducting states programmed by voltage pulses applied to the gate and improved transistor performance make memTs very promising devices to be used as artificial synapses that consume as little energy as 250 pJ per synaptic action (SA). The analog synaptic device concept proposed here can be explored further as the employed organic materials are relatively easy to process and their electronic properties can be tuned as desired using conventional chemical routes and approaches
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