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Motion image feature extraction through voltage modulated memory dynamics in an IGZO thin-film transistor
Yu-Chieh Chen1, Jyu-Teng Lin1, Kuan-Ting Chen1
1Department of Materials Science and Engineering, National Cheng Kung University, Tainan 70101, Taiwan. jenschen@ncku.edu.tw.
Nanoscale Horizons
|March 24, 2025
Summary
This study introduces an indium gallium zinc oxide (IGZO) thin-film transistor (TFT) with fading memory for motion recognition in internet of things (IoT) applications. The novel device achieves 93.9% accuracy in motion direction classification, outperforming conventional methods.
Area of Science:
- Materials Science
- Neuromorphic Engineering
- Computer Vision
Background:
- Motion image recognition is vital for Internet of Things (IoT) but challenged by spatiotemporal data processing.
- Conventional feedforward neural networks (FNNs) struggle with temporal dependencies in motion data.
Purpose of the Study:
- To develop a novel neuromorphic device for efficient time-series data processing in motion recognition.
- To leverage fading memory dynamics in indium gallium zinc oxide (IGZO) thin-film transistors (TFTs) for enhanced feature extraction.
Main Methods:
- Fabrication of an IGZO TFT with a hafnium oxide dielectric layer exhibiting voltage-modulated fading memory.
- Utilizing oxygen vacancy migration for transient current responses and dynamic channel conductance modulation.
- Transforming 4-bit time-series sequences into 16 distinct states for feature extraction.
Main Results:
- Achieved 93.9% motion direction classification accuracy with an optimal pulse height of 2.5 V.
- Demonstrated superior performance compared to static non-volatile memory simulations (49.6% accuracy).
- Introduced the degree of state separation (DS) metric to optimize device operation for temporal data processing.
Conclusions:
- The developed IGZO TFT with fading memory dynamics significantly enhances motion history image feature extraction.
- Optimal device operation balances transient decay and cumulative effects for superior temporal data processing.
- This work provides insights into neuromorphic device design for efficient time-series analysis in IoT applications.
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