Article
Nanoscience & Nanotechnology
Seung-Hyun Oh, Minseok Oh, Seongi Lee, Do-Kyun Kim, Jong-Sung Lee, Sol-Kyu Lee, Seung-Kyun Kang, Young-Chang Joo
Summary: Bioelectronic devices with real-time measurements, biological signal processing, and continuous monitoring are needed. Organic electrochemical transistors (OECTs) are suitable for bioelectronics due to their high transconductance and biocompatibility, but they suffer from delayed response time and low cut-off frequency due to ion migration. We propose OECTs with a nanofiber mat channel, which show improved response time and durability. The nanofiber mat channel provides increased surface area and a fibrous structure, and the hydrogel helps maintain its structure and facilitate material exchange.
ACS APPLIED MATERIALS & INTERFACES
(2023)
Article
Chemistry, Multidisciplinary
Mrinal K. Hota, Suman Chandra, Yongjiu Lei, Xiangming Xu, Mohamed N. Hedhili, Abdul-Hamid Emwas, Osama Shekhah, Mohamed Eddaoudi, Husam N. Alshareef
Summary: Covalent organic framework (COF) thin films have been successfully utilized as channel materials in electrical double-layer (EDL) electrochemical transistors, exhibiting similar dynamic behavior to biological synapses.
ADVANCED FUNCTIONAL MATERIALS
(2022)
Article
Chemistry, Multidisciplinary
Yizhou Zhong, Anil Koklu, Diego Rosas Villalva, Yongcao Zhang, Luis Huerta Hernandez, Maximilian Moser, Rawad K. Hallani, Iain McCulloch, Derya Baran, Sahika Inal
Summary: Organic photodiodes (OPDs) have the potential to be used in flexible, lightweight, and miniaturized photodetectors for wearable applications. However, the current and light responsivity of OPDs are limited, and alternative methods are needed to enhance the signal response. In this study, a miniaturized organic electrochemical transistor (OECT) is integrated with an OPD module to harness the potential of OPDs in acquiring physiological signals. The integrated photodetector (IPD) system uses the light intensity to regulate the OPD voltage output, which in turn modulates the OECT channel current. The high transconductance of the OECT enables efficient voltage-to-current conversion, improving the signal-to-noise ratio at the sensing site. The IPD achieves significantly higher photocurrent and responsivity compared to the standalone OPD, highlighting its potential as a wearable biosensor for detecting weak light-based signals from living systems.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Nanoscience & Nanotechnology
Shunsuke Yamamoto, Anastasios G. Polyravas, Sanggil Han, George G. Malliaras
Summary: This study examines the factors controlling the neuromorphic response of organic electrochemical transistors (OECTs) and highlights the dependence of post-synaptic response timescale on the size of ions in the electrolyte. Modeling further reveals that the transient response of the ionic circuit of the OECT controls the neuromorphic response. These findings provide insights for a more rational design of OECT-based neuromorphic devices.
ADVANCED ELECTRONIC MATERIALS
(2022)
Article
Engineering, Electrical & Electronic
Chuanyu Fu, Hangyuan Cui, Shuo Ke, Yixin Zhu, Xiangjing Wang, Yang Yang, Changjin Wan, Qing Wan
Summary: This letter proposes the use of indium oxide nanofibers as channel layers for neuromorphic transistors, which can emulate basic synaptic functions such as short-term memory. The nonlinear synaptic function and short-term memory characteristic of these transistors are advantageous for high energy-efficient reservoir computing systems. By utilizing these nanofiber neuromorphic transistors, ultra-low energy consumption (15 pJ per reservoir state) and ultra-high accuracy (100%) speech digital recognition have been achieved, demonstrating the great potential of the reservoir computing system for intelligent processing tasks.
IEEE ELECTRON DEVICE LETTERS
(2023)
Article
Chemistry, Multidisciplinary
Matteo Cucchi, Daniela Parker, Eleni Stavrinidou, Paschalis Gkoupidenis, Hans Kleemann
Summary: Next-generation implantable computational devices require stable electronic components that can operate in and interact with electrolytic environments without damage. Organic electrochemical transistors (OECTs) have emerged as suitable options, but achieving integrated circuits (ICs) with OECTs in common electrolytes is difficult due to device interactions. Recent studies have focused on minimizing or harnessing crosstalk in order to overcome this challenge. This article discusses the main challenges, trends, and opportunities for realizing OECT-based circuitry in liquid environments, and analyzes successful approaches in autonomous bioelectronics and information processing, demonstrating the potential of using mixed ionic-electronic conductors (OMIECs) for complex computation and machine learning in liquid.
ADVANCED MATERIALS
(2023)
Article
Chemistry, Multidisciplinary
Dimitrios A. Koutsouras, Morteza Hassanpour Amiri, Paul W. M. Blom, Fabrizio Torricelli, Kamal Asadi, Paschalis Gkoupidenis
Summary: This bio-inspired iontronic multiplexer utilizes organic electrochemical transistors and electrolytes for signal multiplexing without the need for peripheral circuitry or address assignment, which significantly reduces integration complexity in bio-applications. The form factors of OECTs allow for intimate biointerfacing and the electrochemical nature of the communication medium opens new avenues for unconventional multiplexing in bioelectronics, wearables, and neuromorphic computing or sensing.
ADVANCED FUNCTIONAL MATERIALS
(2021)
Article
Nanoscience & Nanotechnology
Yuanying Liang, Haoran Tang, Chunyang Zhang, Chunchen Liu, Linfeng Lan, Fei Huang
Summary: In this study, oxoammonium salts were used as secondary dopants to modulate the performance of PEDOT:PSS-based organic electrochemical transistors. By optimizing the dopant concentrations, a low operation voltage with high device performance was achieved, which is important for long-term monitoring of biological activities. Additionally, the TEMPO+TFSI- dopant showed great capability in modulating the work function and morphology reconstruction of PEDOT:PSS, resulting in improved device performance.
ACS APPLIED MATERIALS & INTERFACES
(2022)
Article
Chemistry, Physical
Jianlong Ji, Hongwang Wang, Ran Liu, Xiaoning Jiang, Qiang Zhang, Yubo Peng, Shengbo Sang, Qijun Sun, Zhong Lin Wang
Summary: The study introduces a novel tuning method for organic electrochemical transistors (OECTs) using a dual-liquid-gate configuration, allowing for precise control of critical parameters and investigation of transient electrical properties and typical neuromorphic behaviors.
Article
Automation & Control Systems
Dapeng Liu, Qianqian Shi, Junyao Zhang, Li Tian, Lize Xiong, Shilei Dai, Jia Huang
Summary: This article reports on an optoelectronic neuromorphic transistor based on a 2D-MOF/polymer charge-trapping layer, and explores the application potential of 2D-MOFs in neuromorphic computing. The results show that 2D-MOFs exhibit excellent charge-trapping properties and achieve various synaptic behaviors and emotion-adjustable learning behavior.
ADVANCED INTELLIGENT SYSTEMS
(2022)
Article
Chemistry, Physical
Si En Ng, Yeow Boon Tay, Terence Yan King Ho, Ankit, Nripan Mathews
Summary: This study demonstrates an electrochromic transistor that can adapt to environmental light conditions and achieve sensitization and desensitization of color detection. Compared to traditional photodetectors, this transistor offers significant advantages in miniaturization of adaptable photodetectors.
Article
Chemistry, Applied
Minhu Huang, Seunghyeon Lee, Il-Young Jo, Hyunbeen Park, Bong Sup Shim, Myung-Han Yoon
Summary: This study investigated the fabrication process and properties of fiber-type organic electrochemical transistors (OECTs). By utilizing composite fibers of poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) and TEMPO-oxidized cellulose nanofiber (CNF), the mechanical strength of the fibers was significantly improved, leading to higher carrier mobility. These findings suggest great potential for eco-friendly wearable/textile electronics.
CARBOHYDRATE POLYMERS
(2024)
Article
Chemistry, Multidisciplinary
Hanlin Wang, Yusheng Chen, Zhenjie Ni, Paolo Samori
Summary: In this study, an electrochemical-electret coupled organic synapse (EECS) with a reversible facilitation-to-depression switch is designed, which can emulate the function of biological synapses. By adjusting the energy level offset, the transition threshold of the device can be tuned for specific applications, providing additional responsiveness. This novel synapse architecture represents a significant advancement in the field of artificial organic synapses and opens up new possibilities for the fabrication of abiotic neural networks.
ADVANCED MATERIALS
(2022)
Article
Chemistry, Multidisciplinary
Michael Skowrons, Drona Dahal, Pushpa Raj Paudel, Bjoern Luessem
Summary: The applicability of the gradual channel approximation in organic electrochemical transistors is studied using a 2D drift-diffusion model. It is found that switching in OECTs is governed by two separate mechanisms - doping/de-doping and the formation of an electrostatic double layer. The balance between these mechanisms depends on the morphology of the mixed conductor.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Chemistry, Physical
Yujie Peng, Lin Gao, Changjian Liu, Jinyi Deng, Miao Xie, Libing Bai, Gang Wang, Yuhua Cheng, Wei Huang, Junsheng Yu
Summary: This work demonstrates stretchable synaptic OECTs using a three-dimensional P3HT/SEBS blend porous elastic film which can fully emulate biological synaptic behaviors. The architecture allows for adjustable OECT output and hysteresis, enabling plasticity transition. The stretchable synaptic OECTs exhibit excellent mechanical robustness at a 30% strain and reliable electrical characteristics after 500 stretching cycles. Furthermore, near-ideal weight updates, symmetric long-term potentiation and depression, and image simulation applications are validated.
Article
Computer Science, Artificial Intelligence
Akiyo Nomura, Megumi Ito, Atsuya Okazaki, Masatoshi Ishii, Sangbum Kim, Junka Okazawa, Kohji Hosokawa, Wilfried Haensch
Summary: Studies have shown that the success of neuromorphic computing is closely related to the conductance steps and device variability of NVM devices, which need to be considered in the design and optimization of synaptic devices. For PCM devices, having more than 500 conductance steps achieves comparable performance, while less than 10% conductance update variation is required to achieve comparable accuracy.
NEURAL PROCESSING LETTERS
(2021)
Review
Multidisciplinary Sciences
Seung Ju Kim, Sang Bum Kim, Ho Won Jang
Summary: Inspired by the human brain, memristor-based neuromorphic computing systems can store multiple values by changing resistance and simulate artificial synapses in brain-inspired computing. Research has shown that these computing systems can learn, infer, and even create using various artificial neural networks.
Article
Engineering, Electrical & Electronic
Deokyoung Kang, Suyeon Jang, Sejeung Choi, Sangbum Kim
Summary: Recent studies on neuromorphic computing have utilized stochastic synapses to implement power-efficient stochastic computing, with this paper proposing to generate stochasticity by exploiting intrinsic 1/f.
SEMICONDUCTOR SCIENCE AND TECHNOLOGY
(2021)
Article
Nanoscience & Nanotechnology
Yangho Jeong, Hyunjoon Lee, Da Gil Ryu, Seong Ho Cho, Gawon Lee, Sangbum Kim, Seyoung Kim, Yun Seog Lee
Summary: This study investigates the ionic programming dynamics and switching mechanisms in metal-oxide ECRAM synaptic devices, aiming to optimize device characteristics and improve computational performance by enhancing ion transport properties. The research suggests that high ionic transport properties in the channel and electrolyte can enhance the performance of metal-oxide ECRAMs, leading to optimized synaptic device characteristics for maximum computation performance.
ADVANCED ELECTRONIC MATERIALS
(2021)
Article
Engineering, Electrical & Electronic
Inhyuk Choi, Sangbum Kim
Summary: The study proposed a novel phase-change memory structure with completely separated program/read paths to achieve the goal of reducing programming power without increasing read time. Through simulation validation, this structure can effectively reduce power consumption, potentially enhancing the cycling endurance of phase-change memory.
MATERIALS SCIENCE IN SEMICONDUCTOR PROCESSING
(2021)
Article
Multidisciplinary Sciences
Jaehyun Kang, Taeyoon Kim, Suman Hu, Jaewook Kim, Joon Young Kwak, Jongkil Park, Jong Keuk Park, Inho Kim, Suyoun Lee, Sangbum Kim, YeonJoo Jeong
Summary: In this paper, a new cluster-type analogue memristor is demonstrated by incorporating Ti nanoclusters into densified amorphous Si, inducing electrochemical reduction activity of Ag cations for linear potentiation/depression with a large conductance range and long data retention. The linearity improvement is selectively tuneable by adjusting the reduction potentials of incorporated metals, and the Ti-4.8%:a-Si device functions as an ideal synaptic model with high accuracy in image processing simulation.
NATURE COMMUNICATIONS
(2022)
Article
Nanoscience & Nanotechnology
Hyunjoon Lee, Da Gil Ryu, Giho Lee, Min-Kyu Song, Hyungjin Moon, Jaehyeong Lee, Jongchan Ryu, Ji-Hoon Kang, Junmin Suh, Sangbum Kim, Jongwoo Lim, Dongsuk Jeon, Seyoung Kim, Jeehwan Kim, Yun Seog Lee
Summary: This study proposes the use of metal-oxide based electrochemical random-access memory (ECRAM) as an analog synaptic device to achieve high-performance and high-integrity cross-point arrays. Different weight update characteristics of ECRAM devices are obtained by adjusting programming voltage pulses. Simulation studies suggest that the performance of neuromorphic computing can be further improved by balancing the weight update characteristics of ECRAM.
ADVANCED ELECTRONIC MATERIALS
(2022)
Review
Materials Science, Multidisciplinary
Kanghyeon Byun, Inhyuk Choi, Soonwan Kwon, Younghoon Kim, Donghoon Kang, Young Woon Cho, Seung Keun Yoon, Sangbum Kim
Summary: Nonvolatile memory (NVM)-based neuromorphic computing has attracted significant attention for its potential in energy-efficient analog computation. This review paper discusses recent advancements in synaptic devices that utilize NVM to improve linearity and symmetry, and also introduces circuit and algorithmic approaches to compensate for non-idealities in analog synaptic devices.
ADVANCED MATERIALS TECHNOLOGIES
(2022)
Article
Chemistry, Physical
Jongmin Park, Tae-Hyeon Kim, Osung Kwon, Muhammad Ismail, Chandreswar Mahata, Yoon Kim, Sangbum Kim, Sungjun Kim
Summary: In this study, we developed a W/HfO2/TiN vertical resistive random-access memory (VRRAM) for neuromorphic computing. The basic electrical properties, conduction mechanism, and current behavior relative to temperature were investigated. The practicality of the device was evaluated using a convolutional neural network, and 8-bit reservoir computing with higher efficiency was achieved.
Article
Computer Science, Hardware & Architecture
Junmo Lee, Joon Hwang, Youngwoon Cho, Min-Kyu Park, Woo Young Choi, Sangbum Kim, Jong-Ho Lee
Summary: This article introduces a hardware-efficient on-chip weight update scheme called CRUS, which algorithmically mitigates the nonlinear weight update in synaptic devices. By introducing the update noise (UN) metric and adjusting the LTD skip conditions, CRUS achieves over 90% accuracy on the MNIST dataset and exhibits robustness to cycle-to-cycle variations.
IEEE JOURNAL ON EXPLORATORY SOLID-STATE COMPUTATIONAL DEVICES AND CIRCUITS
(2022)
Article
Automation & Control Systems
Uicheol Shin, Masatoshi Ishii, Atsuya Okazaki, Megumi Ito, Malte J. Rasch, Wanki Kim, Akiyo Nomura, Wonseok Choi, Dooyong Koh, Kohji Hosokawa, Matthew BrightSky, Seiji Munetoh, SangBum Kim
Summary: This study realizes a fully silicon-integrated restricted Boltzmann machine (RBM) using novel stochastic leaky integrate-and-fire (LIF) neuron circuits and six-transistor/2-PCM-resistor (6T2R) synaptic unit cells on 90 nm CMOS technology. The chip performs data-intensive machine learning tasks in a power-efficient manner by executing computations asynchronously and in parallel.
ADVANCED INTELLIGENT SYSTEMS
(2022)
Review
Materials Science, Multidisciplinary
Jaehyun Kim, Donghoon Kang, Sangbum Kim, Ho Won Jang
Summary: Machine learning offers new shortcuts for materials science research, enabling the study of vast materials space through the establishment of scientific data repositories. Recent advancements in catalyst design benefit from machine learning, and machine learning models can be transferred to applications in different domains.
ACS MATERIALS LETTERS
(2021)
