Modulation of Synaptic Plasticity Mimicked in Al Nanoparticle‐Embedded IGZO Synaptic Transistor

Abstract

Diverse artificial synapse structures and materials are widely proposed for neuromorphic hardware systems beyond von Neumann architecture owing to their capability to mimic complex information processing tasks such as image recognition, natural language processing, and learning. Nevertheless, temporal and spatial randomness in the movement of ion and electron particles that exist in materials usually prevents the solid‐state‐based synaptic devices from enabling the reliable modulation of synaptic plasticity. An aluminum nanoparticle (Al NP)‐embedded indium gallium zinc oxide (IGZO) synaptic transistor whose spike peak level and conductance change can be precisely modulated by the density of Al NPs within the IGZO channel is demonstrated. Essential synaptic functions including excitatory or inhibitory postsynaptic current, paired pulse facilitation, and short‐term potentiation or depression are also thoroughly emulated in the synaptic transistor device with the most optimized Al NP density: IGZO:Al NPs (6 nm). Moreover, controllable switching from short‐term to long‐term memory regimes essential for a learning task is demonstrated. Simulation results prove that this transistor can provide a decent recognition accuracy for neuromorphic computing. Indeed, the integrated IGZO:Al NP synaptic circuit with the effective synaptic plasticity will facilitate the implementation of a reconfigurable neuromorphic computing system.

Emerging Materials for Neuromorphic Devices and Systems

“…By controlling the number of trapped charge carriers, analog memory characteristics can be achieved. A charge-trapping transistor composed of IGZO with Al NPs was suggested as artificial synapse ( Figure 5 H) ( Kim et al., 2020a ). Al NPs were used for charge-trapping sites to control the conductance of the devices.…”

“… (J) Potentiation/depression of charge-trapping transistor. Reproduced with permission ( Kim et al, 2020a ). Copyright 2020, John Wiley & Sons, Inc. …”

“…Two-terminal devices such as memristors, ferroelectric tunnel junctions (FTJs), and phase change memories (PCMs) have been researched as artificial synapses ( Jang et al., 2019a ; Li et al., 2020a ; Kuzum et al., 2012a ). Also, three-terminal devices including electrochemical transistors, ferroelectric transistors, and charge-trapping transistors have been researched as artificial synapses ( Nikam et al., 2019 ; Chou et al., 2020 ; Kim et al., 2020a ). Neural networks require emulation of the function of neurons; these functions have been achieved using memristors, PCMs, threshold switching devices, and ferroelectric transistors ( Hua et al., 2019 ; Tuma et al., 2016 ; Mulaosmanovic et al., 2018a ).…”

Flexible and Energy-efficient Synaptic Transistor with Quasi-linear Weight Update Protocol by Inkjet Printing of Orientated Polar-electret/High-k Oxide Hybrid Dielectric

“…Signi cantly, the achieving of su cient dielectric states is still a burden even for noninkjet-printed synaptic transistors, although tremendous contributions have already been made to achieve the nonvolatile behavior based on diverse kinds of dielectric materials (ferroelectric 11,12 , electret [13][14][15] , electrochemical 16,17 , two-dimensional [18][19][20] , hybrid materials [21][22][23] , etc.) and structures ( oating gate [24][25][26][27][28][29][30] , heterojunction 31-34 , etc. ).…”

Digital and Analog Switching Characteristics of InGaZnO Memristor Depending on Top Electrode Material for Neuromorphic System

“…InGaZnO (IGZO) has been widely used in display and flexible electronics thanks to the advantages of high mobility, uniformity, transparency, and low-temperature processing [34][35][36][37][38][39][40]. In particular, IGZO devices have been recently studied in applications such as wearable healthcare and biosensor, where energy-efficient and flexible logic and memory technologies are needed to process a huge amount of bio-electrical information [41][42][43].…”