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Neuromorphic Computing Devices and Systems Enabled by Two-Dimensional Materials

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Neuromorphic computing is a cutting-edge computational approach that draws inspiration from the architecture and functioning of the human brain, but it requires the materials or hardware to emulate the brain's innate efficiency, adaptability, and parallelism for unlocking its full transformative potential. Two-dimensional materials are a new class of materials that are atomically thin but possess intricate properties such as exceptional electrical conductivity, quantum tenability and energy efficiency. Leveraging these properties holds immense potential for advancing neuromorphic computing, as they enable the creation of ultra-efficient synaptic connections, rapid information processing through quantum effects, and efficient signal conversion and modulation in confined two-dimensional space. This convergence of two-dimensional materials and neuromorphic computing opens avenues for developing brain-inspired computing systems that are not only energy-efficient and highly parallel but also capable of emulating the brain's remarkable adaptability and cognitive prowess.

In this collection, we aim to build on the existing knowledge and further drive the development of multi-functional and highly integrated devices/systems and their applications. The topics include, but are not limited to:

    1) Bioinspired synapses and neurons nanodevices/ systems:

  •  Novel mechanisms of bioinspired devices (e.g., new materials, new device structures)
  •  Applications of bioinspired devices/systems in life sciences
  •  Bioinspired devices/systems for biomimetic applications
  •  A combination of bioinspired devices/ systems and advanced algorithms

    2) 2D material memories for hardware acceleration of neural network algorithms:

  •  High-throughput demonstration of multiply-accumulate operations.
  •  Realization of activation functions (e.g., ReLU, Softmax)
  •  System demonstration based on spiking input.
  •  System demonstration based on physical information input (e.g., optics, mechanics)

    3) All-in-one perception, memory, and computing devices/ systems:

  •  Novel mechanisms of all-in-one devices
  •  Novel systems based on all-in-one devices.
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Editors

Xinran Wang is a Chang Jiang Professor and doctoral adviser at the School of Electronic Science and Engineering at Nanjing University. In 2010 he received his Ph.D. in physics from Stanford University on the synthesis and electronic devices of graphene. His research interests include the growth, physical properties and device applications of two-dimensional materials. He developed the world's first high Ion/Ioff ratio graphene nanoribbon field-effect transistor. From 2010 to 2011, he was a postdoctoral researcher at Stanford University and at the University of Illinois at Urbana-Champaign. In 2011 he returned to Nanjing and started his own laboratory.

 

Peng Zhou is a Full Professor and vice dean of the School of Microelectronics at Fudan University. He received his Ph.D. in physics from Fudan University in 2005. He then worked as a visiting researcher at the Inter-University Semiconductor Research Centre of Seoul National University. In 2005, he joined Fudan University and was appointed professor in 2013. His research interests include novel high-efficiency and low-power electronic devices and integration based on 2D layered materials, focusing on the application of 2D materials in ultrafast memory, including 2D quasi-nonvolatile memory, semi-floating gate memory and synaptic electronics. He is also interested in novel in-memory computing logic devices and integration based on two-dimensional atomic crystals.

 

Mario Lanza is an Associate Professor of the Materials Science and Engineering Programme at King Abdullah University of Science and Technology. In 2010, he received his Ph.D. in electrical engineering from the Universitat Autònoma de Barcelona. His research interests include the integration of two-dimensional materials in solid-state nano/micro-electronic devices and circuits, with a particular focus on memristive crossbar arrays and their use to build artificial neural networks. He is one of the world's leading researchers in the field of hexagonal boron nitride (h- BN) and many other ultrathin dielectrics for electronic devices (SiO2, HfO2, Al2O3). From 2010 to 2013, he was a postdoctoral researcher at Stanford University and Peking University. In 2014, he returned to KAUST and started his own laboratory.

 

Saptarshi Das is an Associate Professor in the Department of Engineering Science and Mechanics (ESM) at Pennsylvania State University. In 2013, he received his Ph.D. in electrical and computer engineering from Purdue University. His research interests include the development of bioinspired and neuromorphic devices for sensing, computing, storage, and security applications using novel nanomaterials such as two-dimensional materials. He was awarded the Young Investigator Award from the United States Air Force Office of Scientific Research and the National Science Foundation (NSF) award CAREER. In 2016, he returned to Penn State and started his own laboratory.

 

Chunsen Liu is an Associate Professor in Frontier Institute of Chip and System, Fudan University. He received the Ph.D. degree from the School of Microelectronics, Fudan University in 2019. His research interests include the innovation of new logic and memory devices and exploration of new electronic system architecture. He developed the first ultrafast bipolar flash memory for self-activated in-memory computing. From 2019 to 2021, he was a postdoctoral researcher at the School of Computer Science, Fudan University. In 2021, he established his own research group as an Associate Professor at Fudan University.

 

Zhihao Yu is a Full Professor in the College of Electronic and Optical Engineering of Nanjing University of Posts and Telecommunications. He obtained his Ph.D. degree in electronic science and technology from Nanjing University, then worked as a postdoctoral researcher in Nanjing University, TSMC Corporate Research and National Chiao Tung University respectively. His research interests include the 2D semiconductor devices and integration. His group firstly developed an in-memory computing architecture based on a duplex two-dimensional material structure for in situ machine learning. In 2020, he returned to Nanjing and started his own group as a full professor.