A Perspective on Tellurium/Selenium-Based Nanomaterials for Neuromorphic Computing

The long-standing von Neumann architecture, while foundational to modern computing, intrinsically suffers from data-transfer inefficiency, which imposes severe limits on speed and energy efficiency in artificial intelligence, machine learning, and real-time data processing. Inspired by the remarkable energy efficiency of the human brain, neuromorphic computing seeks to emulate neural architectures through hardware capable of adaptive learning. Although early complementary metal-oxide-semiconductor (CMOS)-based neuromorphic systems captured basic synaptic behaviors, their narrow dynamic ranges and high operating voltages impede their application prospects. Van der Waals (vdW) materials, particularly tellurium (Te) and Selenium (Se), have recently emerged as promising platforms for next-generation neuromorphic devices due to their intriguing electronic and optoelectronic properties including considerable carrier mobilities, broadband photoresponse, and strong coupling between electrical, optical, and mechanical stimuli. These unique properties offer a direct physical analogy to biological synapses, thereby enabling neuromorphic computing. This Perspective introduces the fundamentals of synaptic behavior and neuromorphic computing and highlights the distinctive properties of Te/Se nanomaterials for synaptic devices. Then we discuss critical advances in Te/Se-based memristors, heterostructures, and electronic/optoelectronic synaptic transistors. In the end, we conclude this perspective with a discussion on the remaining challenges and future opportunities in this evolving field.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2025-12-02
DOI
https://doi.org/10.1021/acsami.5c21759
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

A Perspective on Tellurium/Selenium-Based Nanomaterials for Neuromorphic Computing

Chaoliang Tan, Jiajia Zha, Peng Yang, Haoxin Huang et al.
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
article

A Perspective on Tellurium/Selenium-Based Nanomaterials for Neuromorphic Computing

Chaoliang Tan, Jiajia Zha, Peng Yang, Haoxin Huang, Zhongrui Wang, Liang Zhang, Yuxi Chen
article en

Abstract

The long-standing von Neumann architecture, while foundational to modern computing, intrinsically suffers from data-transfer inefficiency, which imposes severe limits on speed and energy efficiency in artificial intelligence, machine learning, and real-time data processing. Inspired by the remarkable energy efficiency of the human brain, neuromorphic computing seeks to emulate neural architectures through hardware capable of adaptive learning. Although early complementary metal-oxide-semiconductor (CMOS)-based neuromorphic systems captured basic synaptic behaviors, their narrow dynamic ranges and high operating voltages impede their application prospects. Van der Waals (vdW) materials, particularly tellurium (Te) and Selenium (Se), have recently emerged as promising platforms for next-generation neuromorphic devices due to their intriguing electronic and optoelectronic properties including considerable carrier mobilities, broadband photoresponse, and strong coupling between electrical, optical, and mechanical stimuli. These unique properties offer a direct physical analogy to biological synapses, thereby enabling neuromorphic computing. This Perspective introduces the fundamentals of synaptic behavior and neuromorphic computing and highlights the distinctive properties of Te/Se nanomaterials for synaptic devices. Then we discuss critical advances in Te/Se-based memristors, heterostructures, and electronic/optoelectronic synaptic transistors. In the end, we conclude this perspective with a discussion on the remaining challenges and future opportunities in this evolving field.

ACS Applied Materials & InterfacesVol. 17(50)
City University of Hong Kong (HK), Southern University of Science and Technology (CN), Integrated Optoelectronics (Norway) (NO), Institute of Microelectronics (CN), Kunming Institute of Precious Metals (CN), Shenzhen Technology University (CN), United Microelectronics (United States) (US)
Openalex Percentile: Top 62%
Advanced Memory and Neural Computing
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