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Neuromorphic Sensors for Artificial Sense and Next-Generation Robotics
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Neuromorphic Sensors for Artificial Sense and Next-Generation Robotics

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: closed (10 October 2023) | Viewed by 9508

Special Issue Editors

Dr. Dashan Shang

E-Mail Website
Guest Editor
Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
Interests: neuromorphic devices and engineering; brain-inspired computing; memristive system; intelligent sensors
Dr. Changjin Wan

E-Mail Website
Guest Editor
School of Electronic Science & Engineering, Nanjing University, Nanjing 210023, China
Interests: neuromorphic electronics; flexible/stretchable electronics; neuromorphic perceptual systems

Special Issue Information

Dear Colleagues,

Neuromorphic sensors are inspired by the working principles of biological sensory neurons and would play an important role in the Internet of Things in telemedicine, health surveillance, security monitoring, automatic driving, intelligent robots, and so on. The incorporation of advanced sensing technologies that were developed using neuromorphic engineering can endow sensors with biological elements of intelligence such as perception, recognition, and decision making, thus making them suitable for compact, real-time, adaptable, and ultra-low power bio-inspired perceptual systems and robotics. As ideal building blocks, neuromorphic sensors will lead to innovative solutions concerning materials, devices, algorithms, circuitry, and system architectures for Internet of Things application in the future.

This Special Issue plans to cover a wide range of topics, including materials, the fabrication process, working principle of sensors, perception and learning algorithms, intelligent sensing systems, and their application for robotics and artificial sense systems.

Both review articles and original research papers that are to neuromorphic sensors, artificial sense systems, and robotics are welcome.

Dr. Dashan Shang
Dr. Changjin Wan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neuromorphic sensors
  • neuromorphic computing
  • artificial intelligence
  • edge computing
  • artificial neural network
  • spiking neural network
  • machine learning
  • robotics

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Published Papers (4 papers)

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Research

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15 pages, 7298 KiB  
Communication
Ultra-High-Speed Accelerator Architecture for Convolutional Neural Network Based on Processing-in-Memory Using Resistive Random Access Memory
by Hongzhe Wang, Junjie Wang, Hao Hu, Guo Li, Shaogang Hu, Qi Yu, Zhen Liu, Tupei Chen, Shijie Zhou and Yang Liu
Sensors 2023, 23(5), 2401; https://doi.org/10.3390/s23052401 - 21 Feb 2023
Cited by 1 | Viewed by 2142
Abstract
Processing-in-Memory (PIM) based on Resistive Random Access Memory (RRAM) is an emerging acceleration architecture for artificial neural networks. This paper proposes an RRAM PIM accelerator architecture that does not use Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs). Additionally, no additional memory usage is [...] Read more.
Processing-in-Memory (PIM) based on Resistive Random Access Memory (RRAM) is an emerging acceleration architecture for artificial neural networks. This paper proposes an RRAM PIM accelerator architecture that does not use Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs). Additionally, no additional memory usage is required to avoid the need for a large amount of data transportation in convolution computation. Partial quantization is introduced to reduce the accuracy loss. The proposed architecture can substantially reduce the overall power consumption and accelerate computation. The simulation results show that the image recognition rate for the Convolutional Neural Network (CNN) algorithm can reach 284 frames per second at 50 MHz using this architecture. The accuracy of the partial quantization remains almost unchanged compared to the algorithm without quantization. Full article
(This article belongs to the Special Issue Neuromorphic Sensors for Artificial Sense and Next-Generation Robotics)
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11 pages, 3987 KiB  
Article
A 13 µW Analog Front-End with RRAM-Based Lowpass FIR Filter for EEG Signal Detection
by Qirui Ren, Chengying Chen, Danian Dong, Xiaoxin Xu, Yong Chen and Feng Zhang
Sensors 2022, 22(16), 6096; https://doi.org/10.3390/s22166096 - 15 Aug 2022
Cited by 4 | Viewed by 2130
Abstract
This brief presents an analog front-end (AFE) for the detection of electroencephalogram (EEG) signals. The AFE is composed of four sections, chopper-stabilized amplifiers, ripple suppression circuit, RRAM-based lowpass FIR filter, and 8-bit SAR ADC. This is the first time that an RRAM-based lowpass [...] Read more.
This brief presents an analog front-end (AFE) for the detection of electroencephalogram (EEG) signals. The AFE is composed of four sections, chopper-stabilized amplifiers, ripple suppression circuit, RRAM-based lowpass FIR filter, and 8-bit SAR ADC. This is the first time that an RRAM-based lowpass FIR filter has been introduced in an EEG AFE, where the bio-plausible characteristics of RRAM are utilized to analyze signals in the analog domain with high efficiency. The preamp uses the symmetrical OTA structure, reducing power consumption while meeting gain requirements. The ripple suppression circuit greatly improves noise characteristics and offset voltage. The RRAM-based low-pass filter achieves a 40 Hz cutoff frequency, which is suitable for the analysis of EEG signals. The SAR ADC adopts a segmented capacitor structure, effectively reducing the capacitor switching power consumption. The chip prototype is designed in 40 nm CMOS technology. The overall power consumption is approximately 13 µW, achieving ultra-low-power operation. Full article
(This article belongs to the Special Issue Neuromorphic Sensors for Artificial Sense and Next-Generation Robotics)
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19 pages, 11763 KiB  
Article
Electromagnetic Interference Effects of Continuous Waves on Memristors: A Simulation Study
by Guilei Ma, Menghua Man, Yongqiang Zhang and Shanghe Liu
Sensors 2022, 22(15), 5785; https://doi.org/10.3390/s22155785 - 3 Aug 2022
Cited by 2 | Viewed by 1763
Abstract
As two-terminal passive fundamental circuit elements with memory characteristics, memristors are promising devices for applications such as neuromorphic systems, in-memory computing, and tunable RF/microwave circuits. The increasingly complex electromagnetic interference (EMI) environment threatens the reliability of memristor systems. However, various EMI signals’ effects [...] Read more.
As two-terminal passive fundamental circuit elements with memory characteristics, memristors are promising devices for applications such as neuromorphic systems, in-memory computing, and tunable RF/microwave circuits. The increasingly complex electromagnetic interference (EMI) environment threatens the reliability of memristor systems. However, various EMI signals’ effects on memristors are still unclear. This paper selects continuous waves (CWs) as EMI signals. It provides a deeper insight into the interference effect of CWs on the memristor driven by a sinusoidal excitation voltage, as well as a method for investigating the EMI effect of memristors. The optimal memristor model is obtained by the exhaustive traversing of the possible model parameters, and the interference effect of CWs on memristors is quantified based on this model and the proposed evaluation metrics. Simulation results indicate that CW interference may affect the switching time, dynamic range, nonlinearity, symmetry, time to the boundary, and variation of memristance. The specific interference effect depends on the operating mode of the memristor, the amplitude, and the frequency of the CW. This research provides a foundation for evaluating EMI effects and designing electromagnetic protection for memristive neuromorphic systems. Full article
(This article belongs to the Special Issue Neuromorphic Sensors for Artificial Sense and Next-Generation Robotics)
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Review

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29 pages, 4613 KiB  
Review
Research Progress on the Application of Topological Phase Transition Materials in the Field of Memristor and Neuromorphic Computing
by Runqing Zhang, Rui Su, Chenglin Shen, Ruizi Xiao, Weiming Cheng and Xiangshui Miao
Sensors 2023, 23(21), 8838; https://doi.org/10.3390/s23218838 - 30 Oct 2023
Cited by 2 | Viewed by 1818
Abstract
Topological phase transition materials have strong coupling between their charge, spin orbitals, and lattice structure, which makes them have good electrical and magnetic properties, leading to promising applications in the fields of memristive devices. The smaller Gibbs free energy difference between the topological [...] Read more.
Topological phase transition materials have strong coupling between their charge, spin orbitals, and lattice structure, which makes them have good electrical and magnetic properties, leading to promising applications in the fields of memristive devices. The smaller Gibbs free energy difference between the topological phases, the stable oxygen vacancy ordered structure, and the reversible topological phase transition promote the memristive effect, which is more conducive to its application in information storage, information processing, information calculation, and other related fields. In particular, extracting the current resistance or conductance of the two-terminal memristor to convert to the weight of the synapse in the neural network can simulate the behavior of biological synapses in their structure and function. In addition, in order to improve the performance of memristors and better apply them to neuromorphic computing, methods such as ion doping, electrode selection, interface modulation, and preparation process control have been demonstrated in memristors based on topological phase transition materials. At present, it is considered an effective method to obtain a unique resistive switching behavior by improving the process of preparing functional layers, regulating the crystal phase of topological phase transition materials, and constructing interface barrier-dependent devices. In this review, we systematically expound the resistance switching mechanism, resistance switching performance regulation, and neuromorphic computing of topological phase transition memristors, and provide some suggestions for the challenges faced by the development of the next generation of non-volatile memory and brain-like neuromorphic devices based on topological phase transition materials. Full article
(This article belongs to the Special Issue Neuromorphic Sensors for Artificial Sense and Next-Generation Robotics)
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