Low Dimensional Materials for Neuromorphic Computing
A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "D：Materials and Processing".
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 231
Special Issue Editors
Interests: low dimensional materials device and circuit
Interests: quantum computing materials and devices; semiconductor materials and devices; material physics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the development of the Internet of Things, the exponential growth of data has imposed a critical requirement on energy efficiency and processing speed. The power demanded by AI or deep learning computing now doubles every two months. In the past few decades, computing performance has made remarkable improvements through a combination of device size scaling and smart architecture. However, nowadays, they are meeting bottlenecks. On the device level, leakage currents and induced power consumption become an issue as channel length and thickness are approaching the scaling limit. On the architecture level, data shuttling between the information processing and memory units significantly limits the speed and energy efficiency. New materials and computing paradigms were introduced to address these issues associated with data-abundant computing driven by AI. This Special Issue seeks to showcase research papers and review articles that focus on the application of low-dimensional materials in neuromorphic computing, i.e., novel concepts of neuromorphic devices and architecture innovations.
Dr. Xuewei Feng
Dr. Zeheng Wang
Guest Editors
Manuscript Submission Information
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Keywords
- low-dimensional materials: 1D materials, 2D materials, Perovskite, etc.
- neuromorphic computing
- emerging devices for neuromorphic computing: RRAM, ferroelectric memory, memtransistor, phase-change memory, etc.
- neuromorphic computing accelerators
- neuromorphic circuit
