Associative Learning with Oxide-based Electrolyte-gated Transistor Synapses

doi:10.15541/jim20220519

Abstract

Abstract:

The analog channel conductance modulation of electrolyte-gated transistors (EGTs) is a desirable property for the emulation of synaptic weight modulation and thus gives them great potential in neuromorphic computing systems. In this work, an all-solid-state electrochemical EGT was introduced with a low channel conductance (~120 nS) using amorphous Nb₂O₅ and Li-doped SiO₂ (Li_xSiO₂) as the channel and gate electrolyte materials, respectively. By adjusting the applied gate voltage pulse parameters, the reversable and nonvolatile modulation of channel conductance were achieved, which was ascribed to reversible intercalation/deintercalation of Li⁺ ions into/from the Nb₂O₅ lattice. Essential functionalities of synapses, such as the short-term plasticity (STP), long-term plasticity (LTP), and transformation from STP to LTP, were simulated successfully by conductive channel modulation of the EGTs. Based on these characteristics, a simple associative learning circuit was designed by parallel a resistor between the gate and the source terminals. The Pavlovian dog classical conditioning behavior was simulated based on associative learning circuit, where the resistor represented the unconditioned synapse and shared the gate voltage with EGT according to the proportion of its resistance, and the resistance between gate and source for negative feedback regulation of synaptic weights. These results demonstrate the potential of EGT for artificial synaptic devices and provide an insight into hardware implementation of neuromorphic computing systems.

Key words: electrolyte-gated transistor, synapse, synaptic plasticity, associative learning

CLC Number:

TQ174

FANG Renrui, REN Kuan, GUO Zeyu, XU Han, ZHANG Woyu, WANG Fei, ZHANG Peiwen, LI Yue, SHANG Dashan. Associative Learning with Oxide-based Electrolyte-gated Transistor Synapses[J]. Journal of Inorganic Materials, 2023, 38(4): 399-405.

Figures/Tables 5

Fig. 1 Output characteristic curves under different gate voltages with inset showing the optical pattern (a), transfer characteristic curve (b), gate leakage current curve (c), and working mechanism (d-f) of EGT

Fig. 2 Analogy between EGT and biological synapse (a), EPSC under (3 V, 9 s) pulse (b), PPF stimulated by a pair of gate voltage pulses spaced 0.6 s apart (c), PPF index as a function of pulse interval (d)

Fig. 3 Response of EGT to a series of gate voltage pulses of different pulses amplitude (a), pulses width (b), and pulses frequency (c), dependence of conductance change ratio on pulses amplitude (d) and pulses width (e)Colorful figures are available on website

Fig. 4 Effect of long-term enhanced synaptic weights on neuromorphic computational function (a) Long-term potentiation induced by 64 positive gate voltage pulses (3.6 V, 10 ms); (b) long-term depression induced by 64 negative gate voltage pulses (-3.4 V, 10 ms) and taking points 10 s after pulses; (c) cyclic change results for 100 cycles of alternately applied 64 pulses (+3.6 V/-3.4 V, 10 ms) and taking points 10 s after pulses

Fig. 5 (a) Schematic diagram of classical conditioning using EGT and (b) corresponding equivalent circuit, and (c) simulation of biological classical conditioning

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