Tailoring Synaptic Properties of the Band-Engineered Charge Trap Memory for a Flexible Edge Neuromorphic Processor
Biological signals, including neurological signals, are vital for medicine, security, and interface technologies. Their complexity requires high-connectivity and high-performance memory systems, making neuromorphic synaptic devices essential. Inorganic three-terminal devices offer excellent weight linearity and enable back-propagation but lack flexibility due to their rigid structure. Organic counterparts are flexible but suffer from poor reliability and limited bandgap tunability. To overcome these limitations, this study developed a band-engineered charge trap memory (BE-CTM) device using initiated chemical vapor deposition (iCVD), a low-temperature process that enables the formation of uniform hybrid organic-inorganic dielectric layers with a nanometer-scale thickness (≤10 nm) and tunable composition. This hybrid structure combines the mechanical flexibility of organics with the electrical robustness of inorganics, making it ideal for flexible neuromorphic applications. Electrical and reliability tests under biologically relevant voltage pulses confirmed excellent synaptic weight linearity and operational stability under flexible conditions. System-level simulations further demonstrated the device's neuromorphic capability, achieving 93.4% recognition accuracy for handwritten data and 95.8% for noisy images. ECG classification using an MLP model also maintained high accuracy with minimal conductance updates. These results highlight the BE-CTM device's potential for next-generation neuromorphic and biometric information processing.
Authors
- 김민주
- Taehoon Kim (ORCID: https://orcid.org/0000-0003-1155-188X)
- S. B. Choi
- Hyeonji Lee
- Jungyeop Oh
- Jongseo Won
- Min-Kyu Shin
- Woonggi Hong
Institutions
- Korea Advanced Institute of Science and Technology (KR)
- Chung-Ang University (KR)
- Dankook University (KR)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2026-01-13
- DOI
- https://doi.org/10.1021/acsami.5c19280
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00