Dual-Mode Photonic Synapse Based on a Lead-Free 2D Ruddlesden–Popper Perovskite for Neuromorphic Vision
Inspired by the human visual system, photonic synapses with photonic sensing and data memorization offer a promising alternative to traditional von Neumann architectures for neuromorphic computing. This study introduces a multifunctional artificial photonic synapse based on solution-processed PEA<sub>2</sub>SnI<sub>4</sub> 2D Ruddlesden-Popper perovskite. By modulation of the applied bias voltage, the PEA<sub>2</sub>SnI<sub>4</sub> device can switch between two distinct optoelectronic modes. In the absence of bias, the device operates in the photodetector mode, demonstrating a responsivity of 42.4 mA W<sup>-1</sup>. The low dark current of the device allows for a high detectivity of 3.6 × 10<sup>14</sup> Jones and a broad linear dynamic range of 140 dB. Under reverse bias, the device transitions into a synaptic mode, enabling the observation of several synaptic behaviors, including paired-pulse facilitation, long-term potentiation, spike-frequency-dependent plasticity, and spike-number-dependent plasticity. The synaptic behavior is attributed to band alignment and carrier accumulation in the interfacial layer. Moreover, the synaptic performance of the PEA<sub>2</sub>SnI<sub>4</sub> device is further illustrated through simulations of image contrast enhancement and edge detection. This work reveals the potential of PEA<sub>2</sub>SnI<sub>4</sub>-based photonic synapses for next-generation neuromorphic vision systems, offering an energy-efficient and highly adaptable approach to optoelectronic computing applications.
Authors
- Hao‐Cheng Lin
- Yung‐Tang Chuang (ORCID: https://orcid.org/0000-0001-5744-791X)
- Hung-Ming Chen (ORCID: https://orcid.org/0000-0001-8173-3131)
- Yu‐Ching Huang (ORCID: https://orcid.org/0000-0003-4772-8050)
- Hao‐Wu Lin (ORCID: https://orcid.org/0000-0003-4216-7995)
- Chia-Feng Li
- Pei‐En Jan
- Cheng‐Yueh Chen
Institutions
- Ming Chi University of Technology (TW)
- National Tsing Hua University (TW)
- Research Center for Applied Science, Academia Sinica (TW)
- Issues Research (US)
- Academia Sinica (TW)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2025-08-16
- DOI
- https://doi.org/10.1021/acsami.5c10557
- Citations
- 3
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.19
Funders
- National Science and Technology Council