Emerging Nanomaterials for Bio-Realistic Neuromorphic Computing

#nanomaterials #nanotechnology #Neuromorphic #Computing

Brain-inspired computing hardware is emerging as an alternative to silicon complementary metal-oxide semiconductor chips in order to address the energy crisis of rapidly increasing digital data generation and processing. Conventional non-volatile memories can realize highly parallelized in-memory computing in neural networks, but they lack the adaptability and reconfigurability that are the key attributes of low-energy biological systems. To this end, neuromorphic devices based on low-dimensional nanomaterials embody bio-realistic tunable learning, coupled state variables, non-linear responses, and multi-terminal architectures of synapses.^[1-5] In this talk, I will make the case for two-dimensional (2D) nanomaterials for neuromorphic hardware by drawing parallels between fundamental properties, form factors, and required functionalities in artificial synapses, neurons, and network architectures.^[3,6,7] As a few examples, MoS₂ memtransistors show bio-realistic synaptic learning in scalable crossbar arrays for higher-dimensional dynamic neural networks, multiple-agent reinforcement learning, and cerebellum-inspired anomaly detection.^[7,8,9] Anomalous ferroelectricity in bilayer graphene moiré synaptic transistors achieves unprecedented adaptive learning.^[10] Dual-gated, self-aligned Gaussian heterojunction transistors not only realize complex spiking behavior but also enable low-power hardware for machine learning algorithms in edge computing.^[11,12] Bio-realistic spiking neurons can be printed on flexible substrates and demonstrate biocompatibility by direct communication with mouse cerebellum cells.^[13]

Refs: [1] Chem Rev. (2025), [2] Nature Electronics (2024), [3] Advanced Materials (2022), [4] Matter (2022), [5] Nature Nano. (2020), [6] Nature Nano. (2015), [7] Nature (2018), [8] ACS Nano (2026), [9] Nature Comm (2026), [10] Nature (2023), [11] Nature Comm. (2020), [12] Nature Electronics (2023), [13] Nature Nano. (2026)

Dr. Vinod K. Sangwan is a Research Associate Professor in the Department of Materials Science and 
Engineering at Northwestern University (NU). He obtained a B.Tech. in Engineering Physics from the
Indian Institute of Technology Mumbai and a Ph.D. in physics from the University of Maryland (UMD)
College Park. After that, he joined NU as a postdoctoral researcher and subsequently continued there to
develop a career path as a research faculty member. Dr. Sangwan received the 2004 Iskraut Award at 
UMD, received the 2021 IEEE Chicago Outstanding Senior Research and Development Award, and is
currently a 2026 IEEE Distinguished Lecturer for Nanotechnology. His research interests include 
nanoelectronics, neuromorphic computing, renewable energy, and quantum information science. 
He has published over 130 peer-reviewed journal papers in journals such as Science, Nature, Nature 
Nanotechnology, Nature Materials, and Nature Communications, and holds 20 granted and pending 
patents. At NU, he has mentored three dozen students and postdoctoral researchers, and is a co-principal
investigator on multiple grants from NSF and DOE. He enjoys reading, teaching, and outreach activities
, and is a member of IEEE (senior), APS, MRS, AVS, ACS, SPIE, and AAAS.