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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pixelated Nonvolatile Programmable Photonic Integrated Circuits Based on an Inverse Design

Ting Mei, Yajie Chen, Ting Lei, Ruitao Wu et al.
ACS Photonics
Neural Networks and Reservoir Computing
article

Pixelated Nonvolatile Programmable Photonic Integrated Circuits Based on an Inverse Design

Ting Mei, Yajie Chen, Ting Lei, Ruitao Wu, Xiaocong Yuan, Rongyi Lin, Ying Li, Feifan Qiang, Chao Zhao
article en

Abstract

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.

ACS PhotonicsVol. 12(8)
Northwestern Polytechnical University (CN), Shenzhen University (CN), Integrated Optoelectronics (Norway) (NO), Institute of Microelectronics (CN), Ministry of Industry and Information Technology (CN)
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
Affordable and clean energy
Openalex Percentile: Top 91%
Neural Networks and Reservoir Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.