BEOL‐Compatible Tellurium Films for Optically Stimulated and Mechanically Deformable Artificial Synapses

The prevailing von Neumann bottleneck has demanded alternatives capable of more efficiently executing massive data in state-of-the-art digital technologies. Mimicking the human brain's operational principles, various artificial synapse devices have emerged, whose fabrications generally require high-temperature complementary metal-oxide-semiconductor (CMOS) processes. Herein, centimeter-scale tellurium (Te) films-based optoelectronic synaptic devices are explored by a back-end-of-line (BEOL) compatible low-temperature (200 °C) chemical vapor deposition (CVD). The CVD-grown Te films exhibit prominent semiconducting properties such as broadband photo-responsiveness accompanying a large degree of mechanical deformability. These characteristics coupled with their scalable manufacturability realize a comprehensive set of optically-stimulated synaptic plasticity; i.e., excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and short-to-long-term memory conversion, all of which are well preserved even under severe mechanical deformations. A variety of proof-of-concept applications for artificial neural networks (ANNs) are demonstrated employing these deformation-invariant synaptic features; i.e., high-accuracy (≈90%) pattern recognition, associative learning, and machine learning-implemented visual perception. The fundamental mechanism for the synaptic operations is discussed in the context of their persistent photoconductivity (PPC) and its associated memory effect. This study highlights high promise of low-temperature processable semiconductors for emergent neuromorphic architectures with various form factors beyond the conventional CMOS strategy.

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Publication Details

Journal
Small
Published
2025-06-19
DOI
https://doi.org/10.1002/smll.202503131
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

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article

BEOL‐Compatible Tellurium Films for Optically Stimulated and Mechanically Deformable Artificial Synapses

Han-Kyun Shin, Yeonwoong Jung, Chung Won Lee, Changhyeon Yoo et al.
1 citations
Small
Advanced Memory and Neural Computing
0.73
article

BEOL‐Compatible Tellurium Films for Optically Stimulated and Mechanically Deformable Artificial Synapses

Han-Kyun Shin, Yeonwoong Jung, Chung Won Lee, Changhyeon Yoo, Hyo‐Jong Lee, Sang Sub Han, Seung Ju Kim, Jung Han Kim
article en
1 citations

Abstract

The prevailing von Neumann bottleneck has demanded alternatives capable of more efficiently executing massive data in state-of-the-art digital technologies. Mimicking the human brain's operational principles, various artificial synapse devices have emerged, whose fabrications generally require high-temperature complementary metal-oxide-semiconductor (CMOS) processes. Herein, centimeter-scale tellurium (Te) films-based optoelectronic synaptic devices are explored by a back-end-of-line (BEOL) compatible low-temperature (200 °C) chemical vapor deposition (CVD). The CVD-grown Te films exhibit prominent semiconducting properties such as broadband photo-responsiveness accompanying a large degree of mechanical deformability. These characteristics coupled with their scalable manufacturability realize a comprehensive set of optically-stimulated synaptic plasticity; i.e., excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and short-to-long-term memory conversion, all of which are well preserved even under severe mechanical deformations. A variety of proof-of-concept applications for artificial neural networks (ANNs) are demonstrated employing these deformation-invariant synaptic features; i.e., high-accuracy (≈90%) pattern recognition, associative learning, and machine learning-implemented visual perception. The fundamental mechanism for the synaptic operations is discussed in the context of their persistent photoconductivity (PPC) and its associated memory effect. This study highlights high promise of low-temperature processable semiconductors for emergent neuromorphic architectures with various form factors beyond the conventional CMOS strategy.

SmallVol. 21(33)
University of Central Florida (US), University of Southern California (US), Dong-A University (KR)
National Science Foundation, National Research Foundation, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 27%
Advanced Memory and Neural Computing
0.73
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