Neuromorphic Devices for Extreme Environments: State‐Variable Reliability and Stabilization Strategies
ABSTRACT Neuromorphic devices are increasingly considered for edge‐intelligence systems operating in spaceborne, aerospace, nuclear, cryogenic, and other harsh environments, where local decision‐making is constrained by limited power, communication bandwidth, and latency. Reliable operation in these environments requires more than the preservation of basic electrical functionality because neuromorphic computation is physically encoded in material and interfacial state variables. Environmental stress can reshape these variables and alter analog modulation, plasticity, memory retention, threshold dynamics, and readout stability. This review discusses neuromorphic devices for extreme environments from the perspective of state‐variable reliability and stabilization. Radiation, temperature extremes, and vacuum or low‐pressure conditions are examined as major stressors that perturb defect generation, ionic and electronic transport, interfacial chemistry, switching pathways, and signal readout. Emerging space‐relevant factors, including microgravity‐assisted fabrication and mechanical loading, are also considered with respect to device formation, structural integrity, and long‐term reliability. Stabilization strategies are organized across material‐level design, environmental boundary control, and programming or readout compensation. By connecting device‐level degradation to neuromorphic metrics, array operation, soft‐error response, and inference accuracy, this review provides a framework for evaluating and designing reliable neuromorphic hardware under realistic environmental constraints.
Authors
- Wangmyung Choi
- Jaehyun Hur (ORCID: https://orcid.org/0000-0003-1604-6655)
- Taehyun Park (ORCID: https://orcid.org/0000-0001-9119-9518)
- Nicola Gasparini (ORCID: https://orcid.org/0000-0002-3226-8234)
- Hocheon Yoo (ORCID: https://orcid.org/0000-0003-0772-3327)
- Geon Park (ORCID: https://orcid.org/0000-0002-6878-2649)
- Junhyung Cho (ORCID: https://orcid.org/0009-0007-0043-3294)
- Jaechan Song
Institutions
- Gachon University (KR)
- Hanyang University (KR)
- Imperial College London (GB)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2026-10-13
- DOI
- https://doi.org/10.1002/adfm.78949
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea