An Organic Flexible Artificial Bio-Synapses with Long-Term Plasticity for Neuromorphic Computing.

Tian-Yu Wang,Lin Chen,Shi-Jin Ding,David Zhang,Qing-Qing Sun,Peng Zhou,Hao Zhu,Zhen-Yu He

doi:10.3390/mi9050239

Abstract

Artificial synapses, with synaptic plasticity, are the key components of constructing the neuromorphic computing system and mimicking the bio-synaptic function. Traditional synaptic devices are based on silicon and inorganic materials, while organic electronics can open up new opportunities for flexible devices. Here, a flexible artificial synaptic device with an organic functional layer was proposed. The organic device showed good switching behaviors such as ON/OFF ratio over 100 at low operation voltages. The set and reset voltages were lower than 0.5 V and −0.25 V, respectively. The long-term plasticity, spike-timing-dependent plasticity learning rules (STDP), and forgetting function were emulated using the device. The retention times of the excitatory and inhibitory post-synaptic currents were both longer than 60 s. The long-term plasticity was repeatable without noticeable degradation after the application of five voltage pulse cycles to the top electrode. These results indicate that our organic flexible device has the potential to be applied in bio-inspired neuromorphic systems.

Highlights

The human brain can be seen as an effective system that is capable of analyzing complicated tasks through the integration of storage and computation [1,2]
The controllable conductance is related to the transformation and migration of PEDOT+ [25]. These results demonstrate the feasibility of flexible PEDOT:PSS-based resistive random access memory (RRAM) used as artificial synapses for neuromorphic computing and the potential for wearable electronics applications [26,27]
We fabricated a flexible PEDOT:PSS-based artificial synaptic device that exhibited abrupt resistive switching of binary characteristic and gradual muti-level conductance modulation used for mimicking synaptic plasticity

Summary

Introduction

The human brain can be seen as an effective system that is capable of analyzing complicated tasks through the integration of storage and computation [1,2]. It has been widely recognized that fabricating an artificial electronic device with the function of mimicking the behaviors of a bio-synapse is necessary to realize neuromorphic computing. Many synaptic behaviors have been emulated using artificial synaptic devices, including long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation (PPF), and STDP (spike-timing-dependent plasticity) [4]. The conductance of devices should be modulated gradually and simulate weight changes of bio-synapses [5,6], which are the fundamental to achieving synaptic plasticity. Various devices including CMOS transistors, resistive random access memory (RRAM), ferroelectric random access memory (FeRAM), and phase-change memory (PCM) have been demonstrated exhibiting such synaptic behaviors [4,6,7,8]. RRAM with the advantages of high-integration, low-power consumption, and simple structure, has become one of the promising candidates for the applications in neuromorphic computing

Methods

Results

Conclusion