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Related Experiment Video

Updated: Oct 4, 2025

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

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

Published on: August 29, 2025

In-depth analysis on electrical parameters of floating gate IGZO synaptic transistor affecting pattern recognition

Ojun Kwon^1,2, Seyoung Oh^1,2, Heejeong Park^1,2

¹Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea.

|February 11, 2022

Summary

This study introduces an indium gallium zinc oxide (IGZO) synaptic device for neuromorphic systems. Optimizing pulse conditions significantly improves pattern recognition accuracy for handwritten digits.

Keywords:

IGZO floating gate synaptic transistor MNIST pattern recognition accuracy cycle variation linearity synaptic parameters

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Last Updated: Oct 4, 2025

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Published on: August 29, 2025

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Area of Science:

Materials Science
Neuroscience
Electrical Engineering

Background:

Neuromorphic systems require reliable synaptic devices for high-performance computing.
Indium gallium zinc oxide (IGZO) is a promising material for synaptic transistors due to its unique electrical properties.

Purpose of the Study:

To investigate the correlation between electrical parameters of a floating gate IGZO synaptic device and its pattern recognition accuracy.
To explore the impact of various pulse tuning conditions on synaptic device performance.

Main Methods:

Fabrication of a floating gate IGZO synaptic device with an aluminum trapping layer.
Characterization of fundamental synaptic properties including excitatory postsynaptic current, paired pulse facilitation, and memory effects.
Systematic investigation of pulse parameters (voltage, interval, duration, cycling) on conductance modulation.
Evaluation of pattern recognition accuracy using an artificial neural network simulator on the MNIST dataset.

Main Results:

Demonstration of essential synaptic functionalities in the IGZO transistor.
Identification of the significant influence of pulse tuning conditions on conductance updates.
Highlighting the critical role of conductance update nonlinearity and cycle-to-cycle variation in achieving high accuracy.
Achieved a highest recognition rate of 94.06% on the MNIST handwritten digit dataset under optimized pulse conditions.

Conclusions:

The developed IGZO synaptic device exhibits promising characteristics for neuromorphic applications.
Optimizing pulse conditions is crucial for maximizing pattern recognition accuracy.
Further systematic studies on synaptic parameters are necessary for device optimization.