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Threshold-Variation-Tolerant Coupling-Gate α-IGZO Synaptic Transistor for More Reliably Controllable Hardware Neuromorphic System

Dongyeon Kang, Jun Tae Jang, Shinyoung Park, Md. Hasan Raza Ansari, Jong‐Ho Bae, Sung‐Jin Choi, Dong Myong Kim, Changwook Kim, Seongjae Cho, Dae Hwan Kim

2021IEEE Access20 citationsDOIOpen Access PDF

Abstract

Hardware-oriented neuromorphic computing is gaining great deal of interest for highly parallel data processing and superb energy efficiency, as the candidate for replacement of conventional von Neumann computing. In this work, a novel synaptic transistor constructing the neuromorphic system is proposed, fabricated, and characterized. Amorphous indium-gallium-zinc-oxide ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -IGZO) and Al <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> are introduced as the channel and gate dielectric materials, respectively. Along with the high functionality and low-temperature processing viability, geometric peculiarity featuring extended gate structure improves the performances of the proposed transistor as synaptic component in the neuromorphic system. The insight into the substantial effect of optimal device structure design on energy efficiency is highlighted.

Topics & Concepts

Neuromorphic engineeringComputer scienceTransistorEnergy (signal processing)Topology (electrical circuits)Artificial neural networkElectrical engineeringArtificial intelligencePhysicsEngineeringVoltageQuantum mechanicsAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesTransition Metal Oxide Nanomaterials