Panel-Scale Reconfigurable Photonic Interconnects for Scalable AI Computation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918119044284416 |
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| author | Hsueh, Tzu-Chien Lin, Bill Chen, Zijun Fainman, Yeshaiahu |
| author_facet | Hsueh, Tzu-Chien Lin, Bill Chen, Zijun Fainman, Yeshaiahu |
| contents | Panel-scale reconfigurable photonic interconnects on a glass substrate up to 500-mm x 500-mm or larger are envisioned by proposing a novel photonic switch fabric that enables all directional panel-edge-to-panel-edge reach without the need for active repeaters while offering high communication bandwidth, planar-direction reconfigurability, low energy consumption, and compelling data bandwidth density for heterogeneous integration of an in-package AI computing system on a single glass-substrate photonic interposer exceeding thousands of centimeters square. The proposed approach focuses on reconfigurable photonic interconnects, which are integration-compatible with commercial processor chiplets and 3D high-bandwidth memory (HBM) stacks on a large-area glass substrate, to create a novel panel-scale heterogeneously integrated interposer or package enabling low-energy and high-capacity wavelength-division-multiplexing (WDM) optical data links using advanced high-speed optical modulators, broadband photodetectors, novel optical crossbar switches with multi-layer waveguides, and in-package frequency comb sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06079 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Panel-Scale Reconfigurable Photonic Interconnects for Scalable AI Computation Hsueh, Tzu-Chien Lin, Bill Chen, Zijun Fainman, Yeshaiahu Systems and Control Panel-scale reconfigurable photonic interconnects on a glass substrate up to 500-mm x 500-mm or larger are envisioned by proposing a novel photonic switch fabric that enables all directional panel-edge-to-panel-edge reach without the need for active repeaters while offering high communication bandwidth, planar-direction reconfigurability, low energy consumption, and compelling data bandwidth density for heterogeneous integration of an in-package AI computing system on a single glass-substrate photonic interposer exceeding thousands of centimeters square. The proposed approach focuses on reconfigurable photonic interconnects, which are integration-compatible with commercial processor chiplets and 3D high-bandwidth memory (HBM) stacks on a large-area glass substrate, to create a novel panel-scale heterogeneously integrated interposer or package enabling low-energy and high-capacity wavelength-division-multiplexing (WDM) optical data links using advanced high-speed optical modulators, broadband photodetectors, novel optical crossbar switches with multi-layer waveguides, and in-package frequency comb sources. |
| title | Panel-Scale Reconfigurable Photonic Interconnects for Scalable AI Computation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2508.06079 |