Dr Paddy K.L. Chan, Associate Professor of the Department of Mechanical Engineering and his teams had worked on two research topics “Sub-thermionic, ultra-high-gain organic transistors and circuits” and “Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor”. Both papers have been published by Nature Communications on March 26, 2021 and April 30, 2021 respectively.
Details of the papers:
Sub-thermionic, ultra-high-gain organic transistors and circuits
Zhongzhong Luo, Boyu Peng, Junpeng Zeng, Zhihao Yu, Ying Zhao, Jun Xie, Rongfang Lan, Zhong Ma, Lijia Pan, Ke Cao, Yang Lu, Daowei He, Hongkai Ning, Wanqing Meng, Yang Yang, Xiaoqing Chen, Weisheng Li, Jiawei Wang, Danfeng Pan, Xuecou Tu, Wenxing Huo, Xian Huang, Dongquan Shi, Ling Li, Ming Liu, Yi Shi, Xue Feng, Paddy K. L. Chan & Xinran Wang
Article in Nature Communications 12, Article number: 1928 (2021)
Abstract:
The development of organic thin-film transistors (OTFTs) with low power consumption and high gain will advance many flexible electronics. Here, by combining solution-processed monolayer organic crystal, ferroelectric HfZrOx gating and van der Waals fabrication, we realize flexible OTFTs that simultaneously deliver high transconductance and sub-60 mV/dec switching, under one-volt operating voltage. The overall optimization of transconductance, subthreshold swing and output resistance leads to transistor intrinsic gain and amplifier voltage gain over 5.3 × 104 and 1.1 × 104, respectively, which outperform existing technologies using organics, oxides and low-dimensional nanomaterials. We further demonstrate battery-powered, integrated wearable electrocardiogram (ECG) and pulse sensors that can amplify human physiological signal by 900 times with high fidelity. The sensors are capable of detecting weak ECG waves (undetectable even by clinical equipment) and diagnosing arrhythmia and atrial fibrillation. Our sub-thermionic OTFT is promising for battery/wireless powered yet performance demanding applications such as electronic skins and radio-frequency identification tags, among many others.
Link: https://www.nature.com/articles/s41467-021-22192-2
Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor
Xudong Ji, Bryan D. Paulsen, Gary K. K. Chik, Ruiheng Wu, Yuyang Yin, Paddy K. L. Chan & Jonathan Rivnay
Article in Nature Communications 12, Article number: 2480 (2021)
Abstract:
Associative learning, a critical learning principle to improve an individual’s adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.
Link: https://www.nature.com/articles/s41467-021-22680-5