Proton Migration-Modulated n-Doped Poly(benzodifurandione) Organic Electrochemical Transistors Used for Neuromorphic Computing Applications
Contributors
Contact person (2):
Researcher (4):
-
1.
Universitat Jaume I
- 2. Department of Chemical Engineering and Biotechnology, and High-Value Biomaterials Research and Commercialization Center, National Taipei University of Technology, Taipei 106344, Taiwan;
- 3. Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan;
Description
Neuromorphic computing has emerged as a promising technology that can overcome the limitations that traditional systems face in the Big Data era. Organic electrochemical transistors (OECTs) are potential candidates for artificial synapses in neuromorphic hardware. However, due to their ambient instability, n-type OECTs have not been successfully applied to date in organic artificial synapses, limiting the fabrication of complementary logic circuits. In this work, we prove the potential of the n-doped poly[benzodifurandione] (referred to in the literature as PBFDO or n-PBDF) polymer to fabricate high-performance n-type OECTs for neuromorphic applications using protons as the principal migrating ions. We demonstrate that n-PBDF-based OECTs show high stability and dual working modes (accumulation and depletion) in a NaPF6 electrolyte. The devices exhibit resistive switching and synaptic plasticity promoted by the H+ of the electrolyte. The n-PBDF OECTs also show high-quality long-term potentiation (LTP)/depression (LTD) behavior at low gate voltages (0.8 V) and short pulses (50–500 ms). The applicability of n-PBDF OECTs in neuromorphic computing is successfully validated by simulation with a deep neural network (DNN) model for handwritten digit recognition with different Gaussian noise levels. This work opens new avenues for the future development of n-type OECTs for building (bio)electronic circuits, such as (bio)sensing and neuromorphic computing.
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25 ACS Energy-Sanjuan-Proton migration-modulate.pdf
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Additional details
Related works
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- Dataset: 10.5281/zenodo.15788624 (DOI)
Funding
- Agencia Estatal de Investigación
- MEMORIAS TANDEM CON MATERIALES PEROVSKITA/ORGÁNICO PARA COMPUTACIÓN ANALÓGICA ROBUSTA CON MULTIPLES ESTADOS: PREPARACIÓN, CARACTERIZACIÓN ELÉCTRICA Y HERRAMIENTAS DE SIMULACIÓN PID2022-141850OB-C21