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

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<sup>-2</sup>, 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.

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Publication Details

Journal
Small
Published
2025-08-08
DOI
https://doi.org/10.1002/smll.202506440
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.19

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article

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

Young Tea Chun, Hyeonjung Kim, Gwi‐Ryung Son Hong, Seyong Oh et al.
3 citations
Small
Advanced Memory and Neural Computing
2.19
article

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

Young Tea Chun, Hyeonjung Kim, Gwi‐Ryung Son Hong, Seyong Oh, Wangmyung Choi, Won‐Woo Lee, Hocheon Yoo, Jinsu Yoon
article en
3 citations

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<sup>-2</sup>, 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.

SmallVol. 21(48)
Korea Maritime and Ocean University (KR), Hanyang University (KR), Anyang University (KR)
National Research Foundation, Hanyang University, Korea Basic Science Institute, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion
Openalex Percentile: Top 11%
Advanced Memory and Neural Computing
2.19
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