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DPP-DTT Nanowire Phototransistors for Optoelectronic Synapses in EMG and ECG Signal Classification

Authors
Choi, WangmyungYoon, Jin SeokLee, Won WooHong, Gun HoKim, HyeonjungOh, SeyongTea Chun, YoungYoo, Hocheon
Issue Date
Aug-2025
Publisher
Wiley - V C H Verlag GmbbH & Co.
Keywords
Dpp-dtt Nanowire; Electrocardiogram; Electromyography; Neuromorphic Device; Photo-gating Effect; Phototransistor; Physiological Signal Classification; Biomedical Signal Processing; Electrocardiography; Image Recognition; Lithography; Nanowires; Physiology; Threshold Voltage; De-trapping; Diketopyrrolo-pyrrole-dithienylthieno[3,2-b]thiophene Nanowire; Emg Signal; Gate Pulse; Neuromorphic; Neuromorphic Device; Photo-gating Effect; Physiological Signal Classification; Physiological Signals; Signal Classification; Electromyography; Phototransistors
Citation
Small, pp 1 - 11
Pages
11
Indexed
SCIE
SCOPUS
Journal Title
Small
Start Page
1
End Page
11
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/126263
DOI
10.1002/smll.202506440
ISSN
1613-6810
1613-6829
Abstract
A neuromorphic phototransistor based on nanowire-patterned diketopyrrolo-pyrrole-dithienylthieno[3,2-b]thiophene (DPP-DTT) is reported. The nanowires, well-aligned with a width of 460 nm, spacing of 8–11 µm, and height of ≈80 nm, are fabricated using the stamping method of soft lithography and exhibit optically stimulated synaptic behavior. Under blue illumination (455 nm, 0.55 mW cm−2), a photogating effect arises at the DPP-DTT/SiO2 interface, leading to threshold voltage shifts up to 6.4 V as a result of electron trapping at the interface. Negative gate pulses (−7 V) facilitate recombination of the trapped electrons, inducing detrapping and consequently leading to a decrease in the threshold voltage. These two behaviors effectively emulate the processes of potentiation and depression. Efficient trap–detrapping dynamics are facilitated by the unique geometry of the nanowire. Synaptic plasticity is modulated by adjusting stimulus intensity (light pulse: 0.26–1.42 mW cm−2, gate pulse: −6–−9 V), duration (0.3–2.1 s), frequency (0.47–3.33 Hz), and repetition (1–40 cycles), supporting transitions from short- to long-term behavior. The device is evaluated through artificial intelligence classification tasks, including image recognition and time-dependent physiological analysis. It achieves the classification accuracies of 97.4% for MNIST, 93.4% for electromyography (7 classes), 89.0% for electrocardiography (5 classes), and 83.8% for CIFAR-10. © 2025 Elsevier B.V., All rights reserved.
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