Realization of Patternable Neuromorphic Device based on Photo-Curable Polymer Electrolyte-Gated Organic Field-Effect Transistors

Rou-Yi Li^a, Wei-Ting Liao^a, Qun-Gao Chen^a, Ignacio Sanjuán^b, Ning-Cian Hsiao^a, Antonio Guerrero^b, Chu-Chen Chueh^c, Wen-Ya Lee^a

^aDepartment of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei 106, Taiwan

^bInstitute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain

^cDepartment of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan

Proceedings of Neuronics Conference 2025 (Neuronics25)

Tsukuba, Japan, 2025 June 17th - 20th

Organizers: Takashi Tsuchiya, Chu-Chen Chueh, Sabina Spiga and Jung-Yao Chen

Oral, Rou-Yi Li, presentation 020
Publication date: 15th April 2025

Neuromorphic devices, inspired by the brain's ability to process and store information efficiently, have garnered significant attention due to their potential for low-power computing and artificial intelligence applications. Conventional von Neumann architectures face limitations in energy efficiency and parallel processing, making neuromorphic systems a promising alternative for next-generation computing. By emulating synaptic behavior, these devices can enable advanced pattern recognition, learning, and memory functions while significantly reducing energy consumption.

In this study, we present an electrolyte-gated organic field-effect transistor (EGOFET) that utilizes a solid-state polymer electrolyte (SPE) as the gating medium. The SPE is composed of a thiol–ene-assisted photo-cross-linked nitrile butadiene rubber (NBR) network embedded with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). This photocurable polymer electrolyte can be precisely patterned through photolithography, allowing for the fabrication of devices with tailored dimensions. The resulting NBR/LiTFSI-based EGOFET demonstrates outstanding electronic performance, including a high transconductance of 11.9 mS and an impressive on/off ratio of 10⁵ at a scan rate of 40 mV/s.

Moreover, the strong polarity of the photo-cross-linked NBR network, combined with Li-ion diffusion, induces substantial current hysteresis, enabling synaptic-like plasticity and memory functions. This behavior allows the device to replicate key neuromorphic characteristics, making it suitable for artificial synapse applications. Notably, our device achieves a 91.9% recognition accuracy in deep neural network (DNN)-based handwritten digit classification. These findings highlight the potential of solid-state NBR/LiTFSI EGOFETs as energy-efficient and high-performance neuromorphic computing platforms.

References:

[1] Chen Q-G, et al. Organic Solid-State Electrolyte Synaptic Transistors with Photoinduced Thiol–Ene Cross-linked Polymer Electrolytes for Deep Neural Networks. ACS Materials Letters 7, 682-691 (2025).

Acknowledgements:

National Science and Technology Council (NSTC) in Taiwan (Nos. 112-2221-E-027-012-MY3 and 113-2124-M-027-001)
Powerchip Semiconductor Manufacturing Corporation (No. 112A0347)

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