Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Abstract

Associative learning, a critical learning principle to improve an individual’s adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.

Ultralow‐Power Synaptic Transistors Based on Ta2O5/Al2O3 Bilayer Dielectric for Algebraic Arithmetic

“… where t is the interval time, C 1 and C 2 are the initial facilitation magnitude of the rapid and slow phases, respectively, and τ 1 and τ 2 are the characteristic relaxation times of the fast and slow phases, respectively. [ 34 ] According to the fitting equation, τ 1 and τ 2 are calculated as 0.46 and 3.44 s, respectively. Obviously, τ 2 is about an order of magnitude greater than τ 1 , which is consistent with the situation in biological synapses.…”

Ion-Movement-Based Synaptic Device for Brain-Inspired Computing

“…Recently, synaptic devices based on various types of resistive switching devices have been utilized for brain-inspired computing [ 25 , 26 , 27 , 28 , 29 , 30 ]. Brain-inspired computing based on synaptic devices has achieved particular progress from ion-movement-based resistive switching mechanisms such as cation-movement-based filaments [ 31 , 32 , 33 , 34 , 35 , 36 ], anion-movement-based filaments [ 37 , 38 , 39 , 40 , 41 , 42 , 43 ], cation-movement-based ferroelectric polarization reversal [ 44 , 45 , 46 , 47 , 48 , 49 ], and ion-movement-based electrochemical electrolytes [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. These ion-movement-based resistive switching devices can show a gradual change in conductance and nonvolatile characteristics, which have not been implemented in Mott-insulator-based resistive switching devices.…”

Sub-3V, MHz-Class Electrolyte-Gated Transistors and Inverters

“…These devices employ gate potentials to modulate the conductivity of conducting polymers in direct contact with electrolyte. Currently, EGTs are again enjoying a resurgence of interest associated with their applications in physiological recording [8][9][10] , neuromorphic computing [11][12][13][14][15][16] , and biosensing [17][18][19][20][21][22] , as well as in fundamental physics [23][24][25][26] , where the high charge densities achieved in electrolyte-gated semiconductors are leading to exciting discoveries.…”