Reconfigurable optoelectronic sensor with seven-order dynamic response time range for photodetection and neuromorphic visual perception within monolithic device
Photonic synapses that transcend the von Neumann architecture by integrating sensing, memory, and processing capability are fundamentally limited by the trade-off between rapid response (~μs or less) and persistent photoconductivity (>s). This multi-order-of-magnitude gap in response time has made it challenging for any single conventional device to simultaneously fulfill both real-time photodetection and neuromorphic vision perception. Here, we report a monolithic GeSe/CsPbBr₃ heterojunction device featuring a seven-order tunable photoresponse timescale (from 20 μs to over 500 s). The wide temporal tunability originates from the bias-controlled transition between two distinct carrier dynamic regimes within a single heterostructure. At zero bias, the device operates as a self-powered ultrafast photodetector due to drift-dominated transport with a response time of 23 μs rise/20 μs fall, responsivity of 362.4 mA/W @400 nm, 1.5 mA/W @ 808 nm, detectivity of 2.52 × 10 13 @400 nm, 1.04 × 10 11 Jones @808 nm. It supports real-time broadband sensing (365-1,050 nm) with polarization resolution (dichroic ratio: 2.58 @ 808 nm). Under a 0.05 V bias, the device emulates biological synapses with persistent photoconductivity due to the coupling between photocarriers and ionized selenium vacancies, enabling neuromorphic visual perception for applications such as motion trajectory prediction and in-sensor image denoising. The dual-mode reconfiguration enables sensing, memory, and processing capability on a single device, representing a significant advance in multifunctional photonic synapses with exceptional simplicity, performance, and energy efficiency. It opens various possibilities for future adaptive vision systems.
Authors
- Zhixing Gan (ORCID: https://orcid.org/0000-0001-5610-0287)
- Qingfeng Gui
- Lun Yang (ORCID: https://orcid.org/0000-0002-4929-2345)
- Yunsong Di (ORCID: https://orcid.org/0000-0003-2881-0622)
- M Li
- Xiaoming Wen (ORCID: https://orcid.org/0000-0001-8298-483X)
- X Wu
- Yufan Gu
- Baohua Jia
- Zhigang Zhang
- Xiaohui Hu
Institutions
- Nanjing Normal University (CN)
- Nanomaterials Research (United States) (US)
- Collaborative Innovation Center of Advanced Microstructures (CN)
- Jiangsu Maritime Institute (CN)
- Hubei Normal University (CN)
Publication Details
- Journal
- npj Nanophotonics
- Published
- 2026-06-09
- DOI
- https://doi.org/10.1038/s44310-026-00123-7
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangsu Province