Focus on Materials/Devices for Neuromorphic Computing and Spintronics

Guest editors

Guangyu Zhang Institute of Physics CAS
Peng Zhou Fudan University
Yuchao Yang Peking University
Congli He Beijing Normal University
Cengiz S. Ozkan University of California Riverside


Neuromorphic computing is emerging to provide future high energy efficiency brain-like data storage and processing for a number of technologies incorporating self-evolution and learning as well as fault tolerance management and reducing hardware costs by employing integrated circuits including transistors, memristors, and spintronic switches. To meet the demand from especially ever-increasing applications of IoT (internet-of-things) and AI (artificial intelligence), implementation of neuromorphic computing is being explored in neural network architectures for perception and multisensory integration to design AI vision systems, data analytics, autonomous vehicles and robots, and factory process monitoring. Significant advances have been achieved in both hardware implementation and software over the last decade including materials, devices and architecture, systems, and algorithms, which have greatly accelerated the pace of the AI era. Nevertheless, there are many challenges in the field of neuromorphic computing to overcome, such as the ambiguous principle of the neural network; the mechanism of electrical devices, device variability, power consumption, endurance; and the inferior biocompatibility of the hybrid structures. In addition, it is difficult to demonstrate a large-scale integration of the complex bionic systems. Furthermore, spintronic devices are considered to be promising hardware implementations of neuromorphic computing devices (artificial synapses and neurons), due to their low power consumption, high CMOS compatibility, and high scalability.

This Focus Issue will present the advances and developments in the fields of neuromorphic computing and spintronics technologies, aiming at addressing the challenges involved in materials design, device configurations, fabrication processes, large-scale integration, and high-performance applications.

Topics of interest include (but not limited to):

·         New concept devices for neuromorphic computing and spintronics

·         Principles and mechanisms of devices for neuromorphic computing

·         Device optimization (low power consumption, high speed, high linearity and symmetry of weight update, and low variability)

·         Materials engineering, electronic and phononic properties

·         2D Materials, ferroelectrics, phase change materials and metal oxides

·         Advances in spintronic devices, memristors, phase change memory and ferroelectric tunnel junctions, and other novel devices

·         Large-scale system integration, CMOS compatibility

·         Algorithm design and implementation

·         Biocompatibility and flexibility

Submission process

Focus issue articles are subject to the same review process and high standard as regular Materials Futures articles and should be submitted in the same way. Peer review will be organized immediately upon receipt of the submission and will be published online once it is accepted.

For more comprehensive information on preparing your article for submission and the options for submitting your article, please see our Author guidelines.

Articles should be submitted via the online submission form and select “Focus on Materials/Devices for Neuromorphic Computing and Spintronics” in the 'Select Special Issue' drop down box that appears.


Submission Deadline

March 31, 2023


Article charge

All articles in Materials Futures are published on an open access basis. Article publication charges are waived for authors from 2022 to 2024.

    Emerging multimodal memristors for biorealistic neuromorphic applications

    Low-dimensional optoelectronic synaptic devices for neuromorphic vision sensors

    Tunneling magnetoresistance materials and devices for neuromorphic computing

    Intrinsic vacancy in 2D defective semiconductor In2S3 for artificial photonic nociceptor

    Ultrathin SrTiO3-based oxide memristor with both drift and diffusive dynamics as versatile synaptic emulators for neuromorphic computing

    In-materio reservoir computing based on nanowire networks: fundamental, progress, and perspective

    Nanowire-based synaptic devices for neuromorphic computing