Special Issue on Neuromorphic Devices and Applications

Figure

Illustrative sketch of neuromorphic hardware options. Neuromorphic hardware can be implemented via both artificial neural networks (ANNs) and brain-inspired neural networks. Taken from Daniele Ielmini and Stefano Ambrogio 2020 Nanotechnology 31 092001

Guest Editors

Daniele Ielmini Politecnico di Milano, Italy

Tuo-Hung Hou National Chiao Tung University, Taiwan

Jeehwan Kim MIT, USA

Ming Liu IMECAS, China

Manan Suri IIT Delhi, India

Scope

Neuromorphic engineering aims at developing computing systems that mimic the same structure and the same information processing as the human brain. The brain structure is totally different from the traditional von Neumann architecture of the conventional computers, in that neurons and synapses share the same location in the biological network. Also, the information is exchanged via asynchronous spikes, instead of synchronous bit strings in digital computers. Reproducing the brain structure and processes would give rise to cognitive computers with outstanding energy efficiency and the ability to learn and adapt to a changing environment. Achieving this goal, however, will be possible only by developing a novel class of neuromorphic devices that can reproduce the biological brain processes by physical processes. This include electron devices relying of a broad range of materials, such as metal oxides, chalcogenides, ferroelectric and ferromagnetic materials. Various device structures have been explored, including thin films, nanowires/nanodots and van der Waals heterostuctures. Various brain-inspired architectures and algorithms have been implemented in hardware. Still, there is no consensus about the materials and devices that will constitute tomorrow's neuromorphic computing technology.

The scope of this Special Issue of Semiconductor Science and Technology is to provide an overview of the status and outlook of neuromorphic devices and applications. The most recent advances in materials, devices, circuits and algorithms will be collected and the major challenges toward the development of efficient neuromorphic hardware will be addressed. The Special Issue aims at becoming a top reference for the community of neuromorphic students, researchers and engineers.

How to submit

Either go to mc04.manuscriptcentral.com/sst-iop or click on "Submit an article" on the right-hand side of this page, and select "Special Issue Article" as the article type, then "Special issue on Neuromorphic Devices and Applications".

This special issue will include papers submitted to Neuromorphic Computing and Engineering (NCE) and you are welcome to contribute to this journal also. If you wish to contribute to this journal instead, please submit your manuscript here, identifying your contribution to this special issue in the cover letter. The journal is fully Open Access, and all article fees will be waived for 2021 submissions.

IOP Publishing has just launched NCE, the world's first journal dedicated exclusively to all aspects of neuromorphic computing and engineering. At the interface of physics, electrical engineering, materials science, bioscience, chemistry, mathematics and computer science, it will be truly inter- and multidisciplinary.

Important dates and deadlines

Please note the submission deadline is now 31st July 2021, which has been extended to allow authors more time to write their high quality papers.

Papers

Open access
Memristive devices based on single ZnO nanowires—from material synthesis to neuromorphic functionalities

G Milano et al 2022 Semicond. Sci. Technol. 37 034002

Improving the accuracy and robustness of RRAM-based in-memory computing against RRAM hardware noise and adversarial attacks

Sai Kiran Cherupally et al 2022 Semicond. Sci. Technol. 37 034001

STDP implementation using multi-state spin−orbit torque synapse

Hamdam Ghanatian et al 2022 Semicond. Sci. Technol. 37 024004

Dynamic resistive switching devices for neuromorphic computing

Yuting Wu et al 2022 Semicond. Sci. Technol. 37 024003

Dual-mode dendritic devices enhanced neural network based on electrolyte gated transistors

Zhaokun Jing et al 2022 Semicond. Sci. Technol. 37 024002

Compact model of retention characteristics of ferroelectric FinFET synapse with MFIS gate stack

Md Aftab Baig et al 2022 Semicond. Sci. Technol. 37 024001

OxRAM + OTS optimization for binarized neural network hardware implementation

J Minguet Lopez et al 2022 Semicond. Sci. Technol. 37 014001

A three-bit-per-cell via-type resistive random access memory gated metal-oxide semiconductor field-effect transistor non-volatile memory with the FORMing-free characteristic

E Ray Hsieh et al 2021 Semicond. Sci. Technol. 36 124002

1/f noise in amorphous Sb2Te3 for energy-efficient stochastic synapses in neuromorphic computing

Deokyoung Kang et al 2021 Semicond. Sci. Technol. 36 124001

Analysis and mitigation of parasitic resistance effects for analog in-memory neural network acceleration

T Patrick Xiao et al 2021 Semicond. Sci. Technol. 36 114004

Open access
Training of quantized deep neural networks using a magnetic tunnel junction-based synapse

Tzofnat Greenberg-Toledo et al 2021 Semicond. Sci. Technol. 36 114003

Experimental measurement of ungated channel region conductance in a multi-terminal, metal oxide-based ECRAM

Hyunjeong Kwak et al 2021 Semicond. Sci. Technol. 36 114002

Exploiting the electrothermal timescale in PrMnO3 RRAM for a compact, clock-less neuron exhibiting biological spiking patterns

Omkar Phadke et al 2021 Semicond. Sci. Technol. 36 114001

Ferroelectric HfO2-based synaptic devices: recent trends and prospects

Shimeng Yu et al 2021 Semicond. Sci. Technol. 36 104001

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