Event-Trigger Dual-Signal-Mode Flexible Piezotronic Bipolar Junction Transistor With Machine Learning for AI-Sound Recognition

Emad Iranmanesh; Jucai Zhai; Congwei Liao; Zihao Liang; Yong Zhao; Hang Zhou; Shengdong Zhang; Charalampos Doumanidis; Anand Prakash Dwivedi; Kai Wang

doi:10.1109/led.2023.3238065

Event-Trigger Dual-Signal-Mode Flexible Piezotronic Bipolar Junction Transistor With Machine Learning for AI-Sound Recognition

Emad Iranmanesh^*, Jucai Zhai, Congwei Liao, Zihao Liang, Yong Zhao, Hang Zhou, Shengdong Zhang, Charalampos Doumanidis, Anand Prakash Dwivedi, Kai Wang^*

^*Corresponding author for this work

Mechanical Engineering (Robotics)

Research output: Contribution to journal › Article

Abstract

This work reports on a piezotronic n-p-n bipolar heterojunction transistor (PBJT) intended for AI-sound recognition with low power consumption footprint. An organic polymer (Poly (3-hexylthiophene)) as Base is sandwiched between two inorganic piezoelectric semiconductor ZnO layers as Emitter and Collector, respectively to form a vertically-stacked heterogeneous n-p-n BJT, which differs from the traditional BJT in that it can provide either current or voltage as an output signal to favor application’s preference. It can be regarded as two back-to-back auto-biased piezotronic heterojunction diodes where Base-Collector diode is reversely biased and Base-Emitter one is in a forward bias (or vice versa) upon sound wave stimuli. In addition, polarized charges generated at Emitter and Collector sides redistribute and as a result, give a significant enhancement in the output voltage. With machine learning AI, low frequency (LF) sound detection, mapping, and recognition have been demonstrated by this device.

Original language	English
Pages (from-to)	440-443
Journal	IEEE Electron Device Letters
Volume	44
Issue number	3
DOIs	https://doi.org/10.1109/led.2023.3238065
State	Published - 1 Mar 2023

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title = "Event-Trigger Dual-Signal-Mode Flexible Piezotronic Bipolar Junction Transistor With Machine Learning for AI-Sound Recognition",

abstract = "This work reports on a piezotronic n-p-n bipolar heterojunction transistor (PBJT) intended for AI-sound recognition with low power consumption footprint. An organic polymer (Poly (3-hexylthiophene)) as Base is sandwiched between two inorganic piezoelectric semiconductor ZnO layers as Emitter and Collector, respectively to form a vertically-stacked heterogeneous n-p-n BJT, which differs from the traditional BJT in that it can provide either current or voltage as an output signal to favor application{\textquoteright}s preference. It can be regarded as two back-to-back auto-biased piezotronic heterojunction diodes where Base-Collector diode is reversely biased and Base-Emitter one is in a forward bias (or vice versa) upon sound wave stimuli. In addition, polarized charges generated at Emitter and Collector sides redistribute and as a result, give a significant enhancement in the output voltage. With machine learning AI, low frequency (LF) sound detection, mapping, and recognition have been demonstrated by this device.",

author = "Emad Iranmanesh and Jucai Zhai and Congwei Liao and Zihao Liang and Yong Zhao and Hang Zhou and Shengdong Zhang and Charalampos Doumanidis and Dwivedi, {Anand Prakash} and Kai Wang",

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doi = "10.1109/led.2023.3238065",

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T1 - Event-Trigger Dual-Signal-Mode Flexible Piezotronic Bipolar Junction Transistor With Machine Learning for AI-Sound Recognition

AU - Iranmanesh, Emad

AU - Zhai, Jucai

AU - Liao, Congwei

AU - Liang, Zihao

AU - Zhao, Yong

AU - Zhou, Hang

AU - Zhang, Shengdong

AU - Doumanidis, Charalampos

AU - Dwivedi, Anand Prakash

AU - Wang, Kai

PY - 2023/3/1

Y1 - 2023/3/1

N2 - This work reports on a piezotronic n-p-n bipolar heterojunction transistor (PBJT) intended for AI-sound recognition with low power consumption footprint. An organic polymer (Poly (3-hexylthiophene)) as Base is sandwiched between two inorganic piezoelectric semiconductor ZnO layers as Emitter and Collector, respectively to form a vertically-stacked heterogeneous n-p-n BJT, which differs from the traditional BJT in that it can provide either current or voltage as an output signal to favor application’s preference. It can be regarded as two back-to-back auto-biased piezotronic heterojunction diodes where Base-Collector diode is reversely biased and Base-Emitter one is in a forward bias (or vice versa) upon sound wave stimuli. In addition, polarized charges generated at Emitter and Collector sides redistribute and as a result, give a significant enhancement in the output voltage. With machine learning AI, low frequency (LF) sound detection, mapping, and recognition have been demonstrated by this device.

AB - This work reports on a piezotronic n-p-n bipolar heterojunction transistor (PBJT) intended for AI-sound recognition with low power consumption footprint. An organic polymer (Poly (3-hexylthiophene)) as Base is sandwiched between two inorganic piezoelectric semiconductor ZnO layers as Emitter and Collector, respectively to form a vertically-stacked heterogeneous n-p-n BJT, which differs from the traditional BJT in that it can provide either current or voltage as an output signal to favor application’s preference. It can be regarded as two back-to-back auto-biased piezotronic heterojunction diodes where Base-Collector diode is reversely biased and Base-Emitter one is in a forward bias (or vice versa) upon sound wave stimuli. In addition, polarized charges generated at Emitter and Collector sides redistribute and as a result, give a significant enhancement in the output voltage. With machine learning AI, low frequency (LF) sound detection, mapping, and recognition have been demonstrated by this device.

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DO - 10.1109/led.2023.3238065

M3 - 文章

SN - 0741-3106

VL - 44

SP - 440

EP - 443

JO - IEEE Electron Device Letters

JF - IEEE Electron Device Letters

IS - 3

ER -

Event-Trigger Dual-Signal-Mode Flexible Piezotronic Bipolar Junction Transistor With Machine Learning for AI-Sound Recognition

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