Multilevel Photonic Switching in GST-467 for Deep Neural Network Inference
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arXiv
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2025
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| author | Sur, Arpan Saha, Sudipta Lee, Chih-Yu Takeuchi, Ichiro |
| author_facet | Sur, Arpan Saha, Sudipta Lee, Chih-Yu Takeuchi, Ichiro |
| contents | Phase-change materials (PCMs) have emerged as key enablers of non-volatile, ultra-compact photonic switches for energy-efficient deep neural network (DNN) applications. In this work, we investigate the recently discovered $\mathrm{Ge_{4}Sb_{6}Te_{7}}$ (GST-467) as a high-contrast optical PCM and demonstrate its suitability for multi-level photonic computing. The complex refractive indices of amorphous and crystalline GST-467 were experimentally extracted and used to propose a segmented silicon-on-insulator photonic switch optimized at 1550 nm. Three-dimensional FDTD simulations reveal that segmentation significantly enhances the extinction ratio while maintaining low insertion loss, resulting in a more than seven times higher design figure of merit than an unsegmented design. Laser-induced thermo-optical simulations further establish efficient, reversible switching with sub-nJ energy requirements for crystallization and amorphization. Compared with established GST, GSST, and GSS compositions, GST-467 provides the largest transmission contrast and supports up to 48 resolvable optical states. When deployed as multi-level weights in photonic DNN architectures, the GST-467 switch achieves superior classification accuracy on EMNIST and Fashion-MNIST benchmarks. These results position GST-467 as a highly promising PCM for scalable, low-energy photonic computing and neuromorphic hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19105 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multilevel Photonic Switching in GST-467 for Deep Neural Network Inference Sur, Arpan Saha, Sudipta Lee, Chih-Yu Takeuchi, Ichiro Optics Applied Physics Phase-change materials (PCMs) have emerged as key enablers of non-volatile, ultra-compact photonic switches for energy-efficient deep neural network (DNN) applications. In this work, we investigate the recently discovered $\mathrm{Ge_{4}Sb_{6}Te_{7}}$ (GST-467) as a high-contrast optical PCM and demonstrate its suitability for multi-level photonic computing. The complex refractive indices of amorphous and crystalline GST-467 were experimentally extracted and used to propose a segmented silicon-on-insulator photonic switch optimized at 1550 nm. Three-dimensional FDTD simulations reveal that segmentation significantly enhances the extinction ratio while maintaining low insertion loss, resulting in a more than seven times higher design figure of merit than an unsegmented design. Laser-induced thermo-optical simulations further establish efficient, reversible switching with sub-nJ energy requirements for crystallization and amorphization. Compared with established GST, GSST, and GSS compositions, GST-467 provides the largest transmission contrast and supports up to 48 resolvable optical states. When deployed as multi-level weights in photonic DNN architectures, the GST-467 switch achieves superior classification accuracy on EMNIST and Fashion-MNIST benchmarks. These results position GST-467 as a highly promising PCM for scalable, low-energy photonic computing and neuromorphic hardware. |
| title | Multilevel Photonic Switching in GST-467 for Deep Neural Network Inference |
| topic | Optics Applied Physics |
| url | https://arxiv.org/abs/2512.19105 |