Pixelated Nonvolatile Programmable Photonic Integrated Circuits Based on an Inverse Design
Programmable photonic integrated circuits have developed as a key component for a variety of applications, including advancing optical computing, programmable logic operation, and quantum information processing. However, conventional modulation methods have suffered from significant limitations, including restricted refractive index tunability and substantial static power consumption, which impedes their large-scale integration. In this work, we demonstrate ultralow-loss programmable silicon photonics enabled by the phase-change material Sb2S3. Utilizing inverse design techniques, we introduce pixelated Sb2S3 patterns onto a compact device footprint of only 4 × 18 μm to precisely modulate optical transmission. Through targeted programming of these pixel patterns, we experimentally demonstrate four distinct broadband power distribution ratios (90:10, 80:20, 70:30, and 60:40), achieving a maximum extinction ratio of 12 dB at a central wavelength of 1550 nm for the 90:10 configuration. The measured power distribution ratios closely match inverse design predictions, with deviations of approximately 3%. Based on this approach, we further validated the capability of multi-input/output linear optical transformation devices. This scalable, energy-efficient, and nonvolatile photonic platform paves the way for large-scale optical computing, neuromorphic photonics, and next-generation reconfigurable photonic architectures.
Authors
- Ting Mei (ORCID: https://orcid.org/0000-0001-7756-040X)
- Yajie Chen (ORCID: https://orcid.org/0000-0001-5494-4339)
- Ting Lei
- Ruitao Wu (ORCID: https://orcid.org/0000-0002-5801-1162)
- Xiaocong Yuan (ORCID: https://orcid.org/0000-0003-2605-9003)
- Rongyi Lin
- Ying Li (ORCID: https://orcid.org/0000-0001-8689-5578)
- Feifan Qiang
- Chao Zhao (ORCID: https://orcid.org/0009-0004-8887-7778)
Institutions
- Northwestern Polytechnical University (CN)
- Shenzhen University (CN)
- Integrated Optoelectronics (Norway) (NO)
- Institute of Microelectronics (CN)
- Ministry of Industry and Information Technology (CN)
Publication Details
- Journal
- ACS Photonics
- Published
- 2025-07-16
- DOI
- https://doi.org/10.1021/acsphotonics.5c00983
- 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 Guangdong Province
- Shenzhen University
- National Key Research and Development Program of China
- Shenzhen Science and Technology Innovation Program