Collection
Neuromorphic Computing Devices and Systems Enabled by Two-Dimensional Materials
- Submission status
- Closed
- Submission deadline
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.

Editors
Xinran Wang, PhD
Nanjing University, China.
Peng Zhou, PhD
Fudan University, China.
Mario Lanza, PhD
PhD, Associate Professor, King Abdullah University of Science and Technology, Saudi Arabi
Saptarshi Das, PhD
Pennsylvania State University, USA
Chunsen Liu, PhD
Fudan University, China.
Zhihao Yu, PhD
Nanjing University of Posts and Telecommunications, China
Articles
High-throughput numerical modeling of the tunable synaptic behavior in 2D MoS2 memristive devices
- Benjamin Spetzler
- Vinod K. Sangwan
- Martin Ziegler
Giant memory window performance and low power consumption of hexagonal boron nitride monolayer atomristor
- Sung Jin Yang
- Yu-Rim Jeon
- Deji Akinwande
2D materials-based 3D integration for neuromorphic hardware
- Seung Ju Kim
- Hyeon-Ji Lee
- Ho Won Jang
Logic-in-memory application of ferroelectric-based WS2-channel field-effect transistors for improved area and energy efficiency
- Huijun Kim
- Juhwan Park
- Jongwook Jeon
Atomistic description of conductive bridge formation in two-dimensional material based memristor
- Sanchali Mitra
- Santanu Mahapatra
Bio-inspired “Self-denoising” capability of 2D materials incorporated optoelectronic synaptic array
- Molla Manjurul Islam
- Md Sazzadur Rahman
- Tania Roy
Charge transfer mechanism for realization of double negative differential transconductance
- Kyu Hyun Han
- Seung-Hwan Kim
- Hyun-Yong Yu
Stochastic resonance in 2D materials based memristors
- J. B. Roldán
- A. Cantudo
- M. Lanza
Ultra-low power neuromorphic obstacle detection using a two-dimensional materials-based subthreshold transistor
- Kartikey Thakar
- Bipin Rajendran
- Saurabh Lodha
Ionotronic WS2 memtransistors for 6-bit storage and neuromorphic adaptation at high temperature
- Sameer Kumar Mallik
- Roshan Padhan
- Satyaprakash Sahoo
Graphene/MoS2/SiOx memristive synapses for linear weight update
- Adithi Krishnaprasad
- Durjoy Dev
- Tania Roy
Spiking neural networks based on two-dimensional materials
- Juan B. Roldan
- David Maldonado
- Mario Lanza
An ab initio study on resistance switching in hexagonal boron nitride
- Fabian Ducry
- Dominic Waldhoer
- Mathieu Luisier
Multilevel artificial electronic synaptic device of direct grown robust MoS2 based memristor array for in-memory deep neural network
- Muhammad Naqi
- Min Seok Kang
- Sunkook Kim
Hexagonal boron nitride (h-BN) memristor arrays for analog-based machine learning hardware
- Jing Xie
- Sahra Afshari
- Ivan Sanchez Esqueda
Engineering MoSe2/MoS2 heterojunction traps in 2D transistors for multilevel memory, multiscale display, and synaptic functions
- Yeonsu Jeong
- Han Joo Lee
- Seongil Im
Theory of nonvolatile resistive switching in monolayer molybdenum disulfide with passive electrodes
- Sanchali Mitra
- Arnab Kabiraj
- Santanu Mahapatra
Controllable potential barrier for multiple negative-differential-transconductance and its application to multi-valued logic computing
- Seunghwan Seo
- Jiwan Koo
- Jin-Hong Park
Electron-beam-irradiated rhenium disulfide memristors with low variability for neuromorphic computing
- Sifan Li
- Bochang Li
- Kah-Wee Ang