Review
Neurosciences
Mingyue Zeng, Yongli He, Chenxi Zhang, Qing Wan
Summary: Neuromorphic devices with bionic sensory and perceptual functions have great potential in personal healthcare monitoring, neuroprosthetics, and human-machine interfaces. Efforts have been made to incorporate bio-inspired sensing and neuromorphic engineering in the booming artificial intelligence industry. Challenges and opportunities in these fields are being discussed as the emulation of biological sensing and perception systems progresses.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Materials Science, Multidisciplinary
Jiangqiu Wang, Shuangsuo Mao, Shouhui Zhu, Wentao Hou, Feng Yang, Bai Sun
Summary: This article provides a systematic summary of the research progress of biomemristors as synaptic devices and discusses their application prospects and challenges in artificial intelligence.
ORGANIC ELECTRONICS
(2022)
Review
Chemistry, Multidisciplinary
Gang Ge, Qian Wang, Yi-Zhou Zhang, Husam N. Alshareef, Xiaochen Dong
Summary: In the development of flexible electronics, particularly hydrogel-based stretchable ionotronic devices, researchers are exploring the potential of 3D printing for its excellent patterning capability and design complexity. Despite facing challenges in balancing printability, conductivity, and stretchability, this review offers guidelines on utilizing 3D printing to create high-performance devices, focusing on material considerations and printing quality. Various 3D printing methods for hydrogels and design principles are discussed, along with the potential applications in flexible sensors, soft robots, and other devices.
ADVANCED FUNCTIONAL MATERIALS
(2021)
Article
Computer Science, Information Systems
Jo-Eun Kim, Boram Kim, Hui Tae Kwon, Jaesung Kim, Kyungmin Kim, Dong-Wook Park, Yoon Kim
Summary: In this study, an RRAM crossbar array using PPXC as both a resistive switching layer and substrate was fabricated. The devices exhibited stable electrical and mechanical characteristics, and system-level simulations demonstrated the feasibility of the fabricated RRAM array for neuromorphic applications.
Review
Chemistry, Multidisciplinary
Hefei Liu, Yuan Qin, Hung-Yu Chen, Jiangbin Wu, Jiahui Ma, Zhonghao Du, Nan Wang, Jingyi Zou, Sen Lin, Xu Zhang, Yuhao Zhang, Han Wang
Summary: This paper reviews the progress of artificial neuronal devices based on emerging volatile switching materials, focusing on the demonstrated neuron models implemented in these devices and their utilization for computational and sensing applications. Furthermore, it discusses the inspirations from neuroscience and engineering methods to enhance the neuronal dynamics that are yet to be realized in artificial neuronal devices and networks towards achieving the full functionalities of biological neurons.
ADVANCED MATERIALS
(2023)
Review
Chemistry, Multidisciplinary
Shilei Dai, Xu Liu, Youdi Liu, Yutong Xu, Junyao Zhang, Yue Wu, Ping Cheng, Lize Xiong, Jia Huang
Summary: Living organisms possess a mysterious and powerful sensory computing system based on ion activity. The development of iontronic devices in recent years has provided a promising platform for simulating the sensing and computing functions of living organisms. These devices can generate, store, and transmit signals by adjusting ion concentration and distribution, bridging biology and electronics, and offering advantages in sensing and recognition. This review provides an overview of neuromorphic sensory computing by iontronic devices, highlighting concepts and breakthroughs, and discusses challenges and future directions.
ADVANCED MATERIALS
(2023)
Review
Chemistry, Physical
Shi-Jun Liang, Yixiang Li, Bin Cheng, Feng Miao
Summary: In recent decades, advances in material science have allowed for precise control of material structures at the atomic scale and the realization of various low-dimensional heterostructures. These heterostructures have been widely used in electronic, spintronic, and optoelectronic devices with great success. Manipulating the dimensionality and physical properties of low-dimensional materials enables the design of a wide range of low-dimensional artificial heterostructures. The unique physical properties exhibited by these emerging low-dimensional heterostructures provide unprecedented opportunities for neuromorphic computing applications, laying the groundwork for future intelligent systems.
Article
Neurosciences
Bill Zivasatienraj, W. Alan Doolittle
Summary: Research shows that critical temporal compute features and heterogeneous learning in the brain are rarely simulated by neural networks. This paper introduces a model based on memristors that can mimic neuronal behavior and successfully perform tasks such as image classification and pattern recognition.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Engineering, Environmental
Khoa Bui, Giao T. M. Nguyen, Cedric Vancaeyzeele, Frederic Vidal, Xiao Hu, Chaoying Wan, Cedric Plesse
Summary: Ionogels, consisting of ionic liquid confined in a polymer network, exhibit good strain, conductivity, and flexibility. Vitrimer ionogel based on transesterification shows fast network rearrangement, full recovery of properties, and high sensitivity as a strain sensor. It can also be used for the fabrication of multilayer devices and ionic cables.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Sungmin Hwang, Junsu Yu, Geun Ho Lee, Min Suk Song, Jeesoo Chang, Kyung Kyu Min, Taejin Jang, Jong-Ho Lee, Byung-Gook Park, Hyungjin Kim
Summary: This work presents a hardware neural network using capacitor-based synaptic devices. A MOS capacitor structure with a charge trapping layer was developed for the capacitor-based synaptic device. By using the charge occurring during the capacitor's charging and discharging process, multilevel weight values can be implemented. The vector-matrix multiplication (VMM) function was also experimentally verified using a fabricated synapse array based on NAND flash structure.
IEEE ELECTRON DEVICE LETTERS
(2022)
Article
Chemistry, Physical
Kun Jia, Xiang Li, Yecheng Wang
Summary: The breakdown behaviors of a hydrogel-elastomer device under high DC voltage were studied, revealing a new failure mode, electrochemical breakdown, originating from ion-electron exchange at the metal-hydrogel interface. This new failure mode and its transition are determined by three material properties: the electrical breakdown strength of the dielectric elastomer, the capacitance of the metal-hydrogel interface per unit area, and the electrochemical window of the hydrogel electrolyte.
Review
Automation & Control Systems
Yue Wang, Lei Yin, Wen Huang, Yayao Li, Shijie Huang, Yiyue Zhu, Deren Yang, Xiaodong Pi
Summary: Neuromorphic computing shows promise in addressing the von Neumann bottleneck by leveraging self-adaptive learning and parallel computing with reduced energy consumption. Optoelectronic synaptic devices, inspired by recent advances in optogenetics, offer advantages such as wide bandwidth, minimal delay and power loss, and global regulation of multiple synaptic devices. These devices are categorized into optically stimulated, optically assisted, and optical output synaptic devices, with practical applications already being explored and future development prospects outlined.
ADVANCED INTELLIGENT SYSTEMS
(2021)
Article
Mathematics, Interdisciplinary Applications
Geun Ho Lee, Tae-Hyeon Kim, Min Suk Song, Jinwoo Park, Sungjoon Kim, Kyungho Hong, Yoon Kim, Byung-Gook Park, Hyungjin Kim
Summary: This study analyzes the effect of the conductance overlap region of memristors on the recognition accuracy for on-chip learning simulation.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Materials Science, Multidisciplinary
Rudis Ismael Salinas, Po-Chuan Chen, Chao-Yao Yang, Chih-Huang Lai
Summary: This paper systematically reviews the current status of neuromorphic computing techniques employing spintronic materials and devices. Spintronic devices are considered promising candidates for neuromorphic computing due to their intrinsic non-volatility, ultrafast switching dynamics, and scalability. The paper introduces the biological structure of neurons and important models of artificial neural networks, and demonstrates the revolutionary breakthroughs of spintronic-based neuromorphic computation along with their challenges and outlook.
MATERIALS RESEARCH LETTERS
(2023)
Article
Energy & Fuels
Juan Manuel Gonzalez Sopena, Vikram Pakrashi, Bidisha Ghosh
Summary: This paper proposes a short-term wind power forecasting model based on spiking neural networks adapted to the computational abilities of neuromorphic devices. A case study is conducted using real wind power generation data from Ireland, and the results demonstrate the effectiveness and feasibility of the proposed approach.
