Engineering structure, morphology, and properties of organic and hybrid semiconductors for neuromorphic device applications
- Authors
- Lee, Dong Hyun; Ko, Kwanghee; So, Minsu; Jung, Youngjun; Gasparini, Nicola; Yoo, Hocheon; Oh, Seyong; Choi, Junhwan; Kim, Dong Chan
- Issue Date
- Apr-2026
- Publisher
- ELSEVIER SCIENCE SA
- Keywords
- Synaptic devices; Organic semiconductors; Neuromorphic transistors; Memristors; Stretchable neuromorphic devices
- Citation
- Synthetic Metals, v.318, pp 1 - 25
- Pages
- 25
- Indexed
- SCIE
SCOPUS
- Journal Title
- Synthetic Metals
- Volume
- 318
- Start Page
- 1
- End Page
- 25
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/211024
- DOI
- 10.1016/j.synthmet.2026.118120
- ISSN
- 0379-6779
1879-3290
- Abstract
- Neuromorphic devices based on organic and hybrid materials have emerged as promising components for brain-inspired computing, owing to their molecular tunability, morphological diversity, and compatibility with soft and stretchable platforms. In this review, we revisit recent progress in engineering material structures, device architectures, and interfacial morphologies to regulate charge transport, ionic dynamics, and synaptic plasticity in organic and hybrid systems. Particular emphasis is placed on how molecular design, doping strategies, heterojunction formation, and defect engineering enable key plasticity functions, including short- and long-term memory, paired-pulse dynamics, spike-timing-dependent plasticity, and probabilistic signal processing. We further discuss advances in optoelectronic synapses, where photogating, photo-induced doping, and photo-ionic effects provide spectral selectivity, low-voltage programming, and energy-efficient weight updates. We outline the prospects of integrating flexible and stretchable neuromorphic devices into wearable and edge-computing sensor systems, with an emphasis on the unique advantages of organic and hybrid materials for low-voltage operation, mechanical adaptability, and multimodal sensing. This review aims to provide both a timely overview and a forward-looking perspective on structural and morphological engineering strategies that can guide the development of next-generation neuromorphic electronics.
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