Polymer P‐Insulator‐N Optoelectronic Synaptic Transistor With Ultra‐low Energy Consumption and Enhanced Light Response for Neuromorphic Applications
Abstract Developing ultra‐low‐power optoelectronic synaptic devices is of urgent necessity for artificial intelligence and brain‐inspired devices. Here, a P‐ins‐N (p‐type semiconductor, insulator, and n‐type semiconductor) sandwich structure is proposed for an organic optoelectronic synaptic transistor. The p‐type semiconductor is the hole‐transport pathway for the photocurrent, and the n‐type semiconductor acts as a floating‐gate, while the insulator layer between them is employed to improve the photo‐induced charge transfer and reduce charge recombination. The P‐ins‐N synaptic transistor not only has a low operating voltage (−3 V) and a high I ON / I OFF ratio (> 10⁴), but also exhibits enhanced photo‐response performance. It can simulate various biological synaptic functions, such as excitatory postsynaptic current (EPSC) and paired‐pulse facilitation (PPF). Moreover, the conversion from short‐term memory (STM) to long‐term memory (LTM) is achieved by changing the inputs of light pulses. The low energy consumption is 0.003 fJ, and the figure of merit R ( R = E C × V G ) is as low as 1 × 10 −17 J·V. Meanwhile, it also has potential applications in simulating the regulatory effect of infrared light on wound healing and recognizing handwritten digits.
Authors
- Xian Tang (ORCID: https://orcid.org/0009-0006-7139-9444)
- Laju Bu (ORCID: https://orcid.org/0000-0002-7908-9874)
- Jianxun Pan
- Dandan Zhang (ORCID: https://orcid.org/0000-0002-6584-5106)
- Jiangpeng Li (ORCID: https://orcid.org/0000-0001-5201-8622)
- Guanghao Lu (ORCID: https://orcid.org/0000-0001-7829-7308)
- Jinchi Wei (ORCID: https://orcid.org/0000-0002-4079-949X)
- Zechen Liang
- Jihong Shi
- Xianqiang Xie (ORCID: https://orcid.org/0000-0002-2019-3458)
- Bingyu Han
Institutions
- Ministry of Education (TW)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2025-11-07
- DOI
- https://doi.org/10.1002/adfm.202518966
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.46