Memristors Empowering Next‐Generation Human‐Computer Interaction
ABSTRACT With the continuous advancement of artificial intelligence, human‐computer interaction (HCI) is evolving toward intelligent perception, collaborative decision‐making, and natural integration. However, the physical separation of memory and computing units in traditional von Neumann architecture poses critical bottlenecks in terms of power consumption and latency, which limits its ability to meet the requirements of next‐generation HCI systems. Memristors, with their unique advantages of in‐memory computing, synaptic plasticity emulation, and multi‐physical responsiveness, have emerged as a key enabling technology for brain‐inspired perception and interaction systems. This article systematically reviews the latest research progress of memristors in intelligent perception and neuromorphic systems for next‐generation HCI. First, starting from the material system, performance regulation, and application adaptation are analyzed. Second, the strategy of memristors in device design and integration architecture is discussed. Furthermore, the biomimetic perception system based on memristors is emphasized, demonstrating its integration capability at the hardware level of brain‐inspired perception information processing. Finally, it systematically summarizes the research achievements of memristors in the field of HCI applications and discusses the key challenges currently faced. Looking forward, this review suggests that memristors are expected to provide a solid foundation for building high‐performance, low‐power, and biocompatible next‐generation HCI systems.
Authors
- Zelin Cao (ORCID: https://orcid.org/0009-0006-3695-5759)
- Mengna Wang (ORCID: https://orcid.org/0000-0002-6903-8854)
- Chuncai Kong (ORCID: https://orcid.org/0000-0002-7144-4501)
- Bai Sun (ORCID: https://orcid.org/0000-0002-5840-509X)
- Jinlong He (ORCID: https://orcid.org/0000-0001-7349-8135)
- Song Ling Wang (ORCID: https://orcid.org/0000-0003-4226-6870)
- Longhui Fu (ORCID: https://orcid.org/0000-0002-1025-4018)
- Kaikai Gao
- Bo Yang
- Jinyou Shao
- Teng Wu
- Ye Tang
- Kun Wang
- Qi Chen
Institutions
- Fujian Institute of Research on the Structure of Matter (CN)
- Second Affiliated Hospital of Xi'an Jiaotong University (CN)
- State Key Laboratory of Remote Sensing Science (CN)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2026-09-12
- DOI
- https://doi.org/10.1002/adfm.78446
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Xi'an Jiaotong University
- National Natural Science Foundation of China
- Natural Science Basic Research Program of Shaanxi Province