Proton migration-modulated n-doped poly(benzodifurandione) (n-PBDF) organic electrochemical memtransistors used for neuromorphic computing applications

Ignacio Sanjuán^a, David Franco^a, Qun-Gao Chen^b, Chu-Chen Chueh^c, Wen-Ya Lee^b, Antonio Guerrero^a

^aInstitute of Advanced Materials (INAM), Universitat Jaume I, Avinguda de Vicent Sos Baynat, s/n, Castelló de la Plana, Spain

^bDepartment of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei

^cDepartment of Chemical Engineering, National Taiwan University, Taipei

Proceedings of MATSUS Fall 2025 Conference (MATSUSFall25)

D4 Organic Electrochemical Transistors – Materials and Device Properties - #OectMap

València, Spain, 2025 October 20th - 24th

Organizers: Scott Keene, Sabine Ludwigs and Tom van der Pol

Oral, Ignacio Sanjuán, presentation 366
Publication date: 21st July 2025

The explosion of internet usage, the Internet of things (IoT), and the artificial intelligence (AI) is generating a vast amount of data daily that traditional computing systems are not able to handle efficiently. Neuromorphic computing is emerging as a new computing paradigm that can overcome the limitations of silicon-based computing by emulating the functioning of the most efficient computing system known, the human brain.[1] Organic electrochemical memtransistors (OECmTs) are potential candidates to be used as the artificial synapses that the neuromorphic hardware needs.[2] However, OECmTs fabricated with n-type organic mixed ionic-electronic conductors (OMIECs) have not been successfully employed in organic artificial synapses because they usually show instability in ambient conditions.[3] In this work, we prove the potential of the recently developed n-doped poly-[benzodifurandione] (n-PBDF) polymer to fabricate high-performance n-type OECmTs, using protons as the principal migrating ions.[4] The devices exhibit resistive switching and synaptic plasticity leading to high-quality long-term potentiation (LTP)/depression (LTD) functions at low gate voltages and short pulses. The applicability of n-PBDF OECTs in neuromorphic computing is validated by performing simulations with a deep neural network (DNN) model for handwritten digit recognition with different Gaussian noise levels.[5] This work opens new avenues for the future development of n-PBDF-based (bio)electronic circuits for diverse applications such as (bio)sensing and neuromorphic computing.

Acknowledgements:

[1] D. V. Christensen, R. Dittmann, B. Linares-Barranco et al. Neuromorph. Comput. Eng. 2022, 2, 022501

[2] Q.-G. Chen, W.-T. Liao, R.-Y. Li, I. Sanjuán, N.-C. Hsiao, C.-T. Ng, T.-T. Chang, A. Guerrero, C.-C. Chueh, W.-Y. Lee, ACS Mater. Lett., 2025, 7, 682-691

[3] M. Alsufyani, B. Moss, C. E. Tait et al. Adv. Mater. 2024, 36, 2403911

[4] H. Tang, Y. Liang, C. Liu, Z. Hu, Y. Deng, H. Guo, Z. Yu, A. Song, H. Zhao, D. Zhao, Y. Zhang, X. Guo, J. Pei, Y. Ma, Y. Cao, F. Huang, Nature 2022, 611, 271

[5] Manuscript submitted. Available at Zenodo, DOI: 10.5281/zenodo.15697274

The authors acknowledge the funding provided by the project NEUROVISIONM (Generalitat Valenciana, code: MFA/2022/055).

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