Issue

Vol. 17 (2025)

Multifunctional Organic Materials, Devices, and Mechanisms for Neuroscience, Neuromorphic Computing, and Bioelectronics

https://doi.org/10.1007/s40820-025-01756-7
Felix L. Hoch
Faculty of Engineering, University of Southern Denmark, 5230 Odense, Denmark
Qishen Wang
School of Integrated Circuits, Peking University, Beijing 100871, People’s Republic of China
Kian‑Guan Lim
Department of Science, Mathematics, and Technology, and the AI Mega Centre, Singapore University of Technology and Design, Singapore 487372, Singapore
Desmond K. Loke
Department of Science, Mathematics, and Technology, and the AI Mega Centre, Singapore University of Technology and Design, Singapore 487372, Singapore

Corresponding Author: Desmond K. Loke

desmond_loke@sutd.edu.sg

Nano-Micro Letters, Vol. 17 (2025), Article Number: 251

Abstract

Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks. Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems. However, developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge. Organic computational materials offer affordable, biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching. Here, the review investigates the advancements made in the development of organic neuromorphic devices. This review explores resistive switching mechanisms such as interface-regulated filament growth, molecular-electronic dynamics, nanowire-confined filament growth, and vacancy-assisted ion migration, while proposing methodologies to enhance state retention and conductance adjustment. The survey examines the challenges faced in implementing low-power neuromorphic computing, e.g., reducing device size and improving switching time. The review analyses the potential of these materials in adjustable, flexible, and low-power consumption applications, viz. biohybrid spiking circuits interacting with biological systems, systems that respond to specific events, robotics, intelligent agents, neuromorphic computing, neuromorphic bioelectronics, neuroscience, and other applications, and prospects of this technology.


Highlights:
1 The review emphasizes the switching mechanisms of organic neuromorphic materials.
2 In addition to these switching mechanisms, the capabilities of organic neuromorphic materials in tunable, conformable, and low-power applications, e.g., neuromorphic computing, neuroscience, and neuromorphic bioelectronics, are investigated.
3 This review article offers a comprehensive examination of the capabilities of organic neuromorphic material-based devices to incorporate artificial intelligence into everyday activities.

Keywords

Resistive switching mechanisms Organic materials Brain-inspired neuromorphic computing Neuroscience Neuromorphic bioelectronics
Hoch, Felix L., Qishen Wang, Kian‑Guan Lim, and Desmond K. Loke. 2025. “Multifunctional Organic Materials, Devices, and Mechanisms for Neuroscience, Neuromorphic Computing, and Bioelectronics”. Nano-Micro Letters 17 (May):251. https://doi.org/10.1007/s40820-025-01756-7.
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