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Abstract Reconfigurability of photonic integrated circuits (PICs) has become increasingly important due to the growing demands for electronic–photonic systems on a chip driven by emerging applications, including neuromorphic computing, quantum information, and microwave photonics. Success in these fields usually requires highly scalable photonic switching units as essential building blocks. Current photonic switches, however, mainly rely on materials with weak, volatile thermo‐optic or electro‐optic modulation effects, resulting in large footprints and high energy consumption. As a promising alternative, chalcogenide phase‐change materials (PCMs) exhibit strong optical modulation in a static, self‐holding fashion, but the scalability of present PCM‐integrated photonic applications is still limited by the poor optical or electrical actuation approaches. Here, with phase transitions actuated by in situ silicon PIN diode heaters, scalable nonvolatile electrically reconfigurable photonic switches using PCM‐clad silicon waveguides and microring resonators are demonstrated. As a result, intrinsically compact and energy‐efficient switching units operated with low driving voltages, near‐zero additional loss, and reversible switching with high endurance are obtained in a complementary metal‐oxide‐semiconductor (CMOS)‐compatible process. This work can potentially enable very large‐scale CMOS‐integrated programmable electronic–photonic systems such as optical neural networks and general‐purpose integrated photonic processors.
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Self-controlling photonic-on-chip networks with deep reinforcement learning
Abstract We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.
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- Award ID(s):
- 1650499
- PAR ID:
- 10317242
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-021-02583-7
