Photonic Synaptic Array Based on Phase‐change Materials With a High‐Efficiency and Easily Integrable Microheater
ABSTRACT Phase‐change materials (PCMs) combine non‐volatile switching with large optical contrast, enabling PCM‐integrated silicon photonics to achieve a reduced footprint and near‐zero static power consumption—features highly attractive for scalable photonic integrated circuits (PICs). However, microheaters for tuning PCMs reported to date are typically designed for individual devices. To avoid modifying existing PIC structures, it is crucial to develop an arrayed microheater with high heating efficiency and easy integrability. Here, we propose an easily integrable array microheater with high thermal efficiency by adopting a double‐layer design comprising a trench‐heating structure and an indium tin oxide (ITO) isolation layer. A 3 × 3 PCM‐based optical synaptic array incorporating the proposed microheater array has been fabricated. Under electrical pulse excitation, all array elements exhibit repeatable switching between the amorphous and crystalline states. Moreover, gradual multilevel modulation is achieved through precise pulse design, with a programming precision exceeding 5 bits for all elements. Finally, we evaluate the photonic synaptic array for optical neural networks and demonstrate a high classification accuracy of 95.98% on a handwritten‐digit recognition task. This work develops a novel electrically reconfigurable microheater array and offers a promising route toward the future scaling of electrically reconfigurable PICs.
Authors
- 董云肖
- Zi Wang (ORCID: https://orcid.org/0000-0002-3953-0493)
- Jian Xia
- Tianci Wang
- Rui Yang (ORCID: https://orcid.org/0000-0002-1886-5286)
- Xiangshui Miao
Institutions
- Yangtze Optical Electronic (China) (CN)
- Hubei University of Automotive Technology (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Laser & Photonics Review
- Published
- 2026-05-27
- DOI
- https://doi.org/10.1002/lpor.71356
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China