Dual Strategy of Molecular-Weight Control and Ionic Doping in Poly(benzodifurandione) for Energy-Efficient Neuromorphic Organic Electrochemical Transistors
Abstract Poly(benzodifurandione) (PBFDO) is a promising n-type mixed conductor for organic electrochemical transistors (OECTs), but its high intrinsic conductivity results in excessive operating currents and energy consumption for neuromorphic computing. Here, we combine molecular-weight engineering and ionic doping to overcome this limitation. Benzofuranone end-capping produces a reduced chain length polymer (PBFDO-BF) with substantially lower intrinsic conductivity, while LiTFSI doping enhances ion-mediated conductance modulation and synaptic functionality. PBFDO-BF + LiTFSI provides enhanced OECT modulation and spike-dependent plasticity while reducing operating currents by approximately 1 order of magnitude compared with pristine PBFDO. In the Modified National Institute of Standards and Technology (MNIST)-based convolutional neural network simulations, the device achieves 97.8% training and 98.6% inference accuracy, with the lowest cumulative energy consumption to reach ≈90% accuracy. These results establish molecular-weight control combined with ionic doping as an effective strategy for developing energy-efficient PBFDO-based neuromorphic OECTs without compromising stability or solution processability.
Authors
- B.R. Ilyassov (ORCID: https://orcid.org/0000-0003-4563-2004)
- Chu‐Chen Chueh (ORCID: https://orcid.org/0000-0003-1203-4227)
- David Franco
- Wen‐Ya Lee (ORCID: https://orcid.org/0000-0003-4562-4813)
- Antonio Guerrero (ORCID: https://orcid.org/0000-0001-8602-1248)
- Qun‐Gao Chen (ORCID: https://orcid.org/0000-0003-2475-8230)
- José Carlos Pérez‐Martínez (ORCID: https://orcid.org/0000-0001-8762-1907)
- Ignacio Sanjuán
- Isaac Sánchez-Márquez
Institutions
- Universitat Jaume I (ES)
- National Taipei University of Technology (TW)
- National Taiwan University (TW)
Publication Details
- Journal
- ACS Energy Letters
- Published
- 2026-09-29
- DOI
- https://doi.org/10.1021/acsenergylett.6c02367
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00