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MOS Capacitor

MOS Capacitor

A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...

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Updated: Jan 7, 2026

Fabrication of a Solution-gated Indium-Tin-Oxide-based One-piece Transistor Enabling Sensitive Biosensing

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Polycrystalline InGaO Thin-Film Transistor with SiO₂ Gate Insulator for High-Performance Artificial Synapses.

Taebin Lim¹, Solbee Lee¹, Heerak Wi²

¹Advanced Display Research Center (ADRC), Department of Information Display, Kyung Hee University, Seoul 02447, South Korea.

ACS Applied Materials & Interfaces

|January 5, 2026

Summary

We developed a new polycrystalline InGaO (C-IGO) synaptic transistor for neuromorphic computing. This device shows high performance and accuracy in AI applications, overcoming limitations of previous ferroelectric and electrolyte-based transistors.

Keywords:

O2 annealing counterclockwise hysteresis oxygen vacancy migration polycrystalline InGaO synaptic transistor

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Area of Science:

Materials Science
Electronics Engineering
Computer Science

Background:

Metal-oxide semiconductor (MOS)-based synaptic transistors are key for neuromorphic chips.
Ferroelectric and electrolyte materials face scalability and integration challenges.

Purpose of the Study:

To report a novel synaptic thin-film transistor (TFT) using polycrystalline InGaO (C-IGO).
To demonstrate the potential of C-IGO TFTs for high-density neuromorphic and AI applications.

Main Methods:

Fabrication of a synaptic TFT based on C-IGO semiconductor.
Characterization of transistor hysteresis, memory window, and synaptic behavior (LTP/LTD).
Simulation of MNIST handwritten digit recognition using device characteristics.

Main Results:

The C-IGO TFT exhibited a large counterclockwise hysteresis (57% memory window) due to oxygen vacancy migration.
Demonstrated high synaptic performance with long-term potentiation/depression characteristics.
Achieved 91.64% accuracy in MNIST recognition simulations.

Conclusions:

C-IGO synaptic TFTs offer a scalable solution compatible with back-end-of-line processes.
The device shows significant potential for advanced neuromorphic and AI hardware.
Oxygen vacancy migration is identified as the mechanism for synaptic behavior.