All‐Sputtered Oxide n‐p Heterojunction Anti‐Ambipolar Transistors for Semantic Segmentation Networks

ABSTRACT Neuromorphic vision provides an efficient route for extracting meaningful information from visual inputs and supporting intelligent perception in dynamic environments. However, visual scenes acquired under excessive or insufficient illumination often contain distorted contrast, weakened boundary information, and shifted input distributions, which degrade semantic segmentation accuracy before network computation. Conventional synaptic or neuromorphic hardware systems usually require additional normalization or preprocessing steps to compensate for such input distortion, limiting the direct integration of signal regulation at the device level. In this work, an all‐sputtered oxide anti‐ambipolar transistor (AAT) based on an IGZO/TeSeO x heterojunction is introduced as a device‐level preprocessing element for illumination‐robust neuromorphic vision. IGZO/TeSeO x AAT exhibits a well‐defined Gaussian‐like anti‐ambipolar transfer characteristic, with a peak‐to‐valley current ratio of 1.17 × 10 3 and a maximum negative transconductance of −240.8 nS, enabling effective attenuation of excessive input signals. The resulting Gaussian‐like anti‐ambipolar response is used to construct Gaussian smoothing and Laplacian of Gaussian filtering kernels. Applied to semantic segmentation under overexposed and underexposed conditions, the proposed preprocessing scheme improves pixel‐wise classification consistency and achieves 86.1% pixel accuracy.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-10-15
DOI
https://doi.org/10.1002/adfm.79037
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

All‐Sputtered Oxide n‐p Heterojunction Anti‐Ambipolar Transistors for Semantic Segmentation Networks

Donghyun Kang, Wangmyung Choi, Il‐Doo Kim, Hyoungsub Kim et al.
Advanced Functional Materials
Advanced Memory and Neural Computing
article

All‐Sputtered Oxide n‐p Heterojunction Anti‐Ambipolar Transistors for Semantic Segmentation Networks

Donghyun Kang, Wangmyung Choi, Il‐Doo Kim, Hyoungsub Kim, Seong Cheol Jang, Hyunho Jang, San Nam, Hocheon Yoo, Seungwoo Baek, Hojeong Jo
article en

Abstract

ABSTRACT Neuromorphic vision provides an efficient route for extracting meaningful information from visual inputs and supporting intelligent perception in dynamic environments. However, visual scenes acquired under excessive or insufficient illumination often contain distorted contrast, weakened boundary information, and shifted input distributions, which degrade semantic segmentation accuracy before network computation. Conventional synaptic or neuromorphic hardware systems usually require additional normalization or preprocessing steps to compensate for such input distortion, limiting the direct integration of signal regulation at the device level. In this work, an all‐sputtered oxide anti‐ambipolar transistor (AAT) based on an IGZO/TeSeO x heterojunction is introduced as a device‐level preprocessing element for illumination‐robust neuromorphic vision. IGZO/TeSeO x AAT exhibits a well‐defined Gaussian‐like anti‐ambipolar transfer characteristic, with a peak‐to‐valley current ratio of 1.17 × 10 3 and a maximum negative transconductance of −240.8 nS, enabling effective attenuation of excessive input signals. The resulting Gaussian‐like anti‐ambipolar response is used to construct Gaussian smoothing and Laplacian of Gaussian filtering kernels. Applied to semantic segmentation under overexposed and underexposed conditions, the proposed preprocessing scheme improves pixel‐wise classification consistency and achieves 86.1% pixel accuracy.

Advanced Functional Materials
Dongguk University (KR), Hanyang University (KR), Sungkyunkwan University (KR)
Openalex Percentile: Top 24%
Advanced Memory and Neural Computing
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