Sander van Haagen*, Salahuddin Nur and Ryoichi Ishihara,
{"title":"深度学习优化,可扩展片上金刚石量子系统的制造容错光子晶体纳米束腔","authors":"Sander van Haagen*, Salahuddin Nur and Ryoichi Ishihara, ","doi":"10.1021/acsaom.5c0006010.1021/acsaom.5c00060","DOIUrl":null,"url":null,"abstract":"<p >Cavity-enhanced diamond color center qubits can be initialized, manipulated, entangled, and read individually with high fidelity, which makes them ideal for large-scale, modular quantum computers, quantum networks, and distributed quantum sensing systems. However, diamond’s unique material properties pose significant challenges in manufacturing nanophotonic devices, leading to fabrication-induced structural imperfections and inaccuracies in defect implantation, which hinder reproducibility, degrade optical properties and compromise the spatial coupling of color centers to small mode-volume cavities. A cavity design tolerant to fabrication imperfections─such as surface roughness, sidewall slant, and nonoptimal emitter positioning─can improve coupling efficiency while simplifying fabrication. To address this challenge, a deep learning-based optimization methodology is developed to enhance the fabrication error tolerance of nanophotonic devices. Convolutional neural networks (CNNs) are applied to promising designs, such as L2 and fishbone nanobeam cavities, predicting <i>Q</i>-factors at least one-million times faster than traditional finite-difference time-domain (FDTD) simulations, enabling efficient optimization of complex, high-dimensional parameter spaces. The CNNs achieve prediction errors below 3.99% and correlation coefficients up to 0.988. Optimized structures demonstrate a 52% reduction in <i>Q</i>-factor degradation, achieving quality factors of 5 × 10<sup>4</sup> under real-world conditions and a 2-fold expansion in field distribution, enabling efficient coupling of nonoptimally positioned emitters. Compared to previous deep-learning optimization methods, this approach achieves twice the <i>Q</i>-factor performance in the presence of fabrication errors, significantly enhancing device robustness. Hence, this methodology enables scalable, high-yield manufacturing of robust nanophotonic devices, including the cavity-enhanced diamond quantum systems developed in this study.</p>","PeriodicalId":29803,"journal":{"name":"ACS Applied Optical Materials","volume":"3 4","pages":"998–1010 998–1010"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsaom.5c00060","citationCount":"0","resultStr":"{\"title\":\"Deep Learning-Optimized, Fabrication Error-Tolerant Photonic Crystal Nanobeam Cavities for Scalable On-Chip Diamond Quantum Systems\",\"authors\":\"Sander van Haagen*, Salahuddin Nur and Ryoichi Ishihara, \",\"doi\":\"10.1021/acsaom.5c0006010.1021/acsaom.5c00060\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Cavity-enhanced diamond color center qubits can be initialized, manipulated, entangled, and read individually with high fidelity, which makes them ideal for large-scale, modular quantum computers, quantum networks, and distributed quantum sensing systems. However, diamond’s unique material properties pose significant challenges in manufacturing nanophotonic devices, leading to fabrication-induced structural imperfections and inaccuracies in defect implantation, which hinder reproducibility, degrade optical properties and compromise the spatial coupling of color centers to small mode-volume cavities. A cavity design tolerant to fabrication imperfections─such as surface roughness, sidewall slant, and nonoptimal emitter positioning─can improve coupling efficiency while simplifying fabrication. To address this challenge, a deep learning-based optimization methodology is developed to enhance the fabrication error tolerance of nanophotonic devices. Convolutional neural networks (CNNs) are applied to promising designs, such as L2 and fishbone nanobeam cavities, predicting <i>Q</i>-factors at least one-million times faster than traditional finite-difference time-domain (FDTD) simulations, enabling efficient optimization of complex, high-dimensional parameter spaces. The CNNs achieve prediction errors below 3.99% and correlation coefficients up to 0.988. Optimized structures demonstrate a 52% reduction in <i>Q</i>-factor degradation, achieving quality factors of 5 × 10<sup>4</sup> under real-world conditions and a 2-fold expansion in field distribution, enabling efficient coupling of nonoptimally positioned emitters. Compared to previous deep-learning optimization methods, this approach achieves twice the <i>Q</i>-factor performance in the presence of fabrication errors, significantly enhancing device robustness. Hence, this methodology enables scalable, high-yield manufacturing of robust nanophotonic devices, including the cavity-enhanced diamond quantum systems developed in this study.</p>\",\"PeriodicalId\":29803,\"journal\":{\"name\":\"ACS Applied Optical Materials\",\"volume\":\"3 4\",\"pages\":\"998–1010 998–1010\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://pubs.acs.org/doi/epdf/10.1021/acsaom.5c00060\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Optical Materials\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acsaom.5c00060\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Optical Materials","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsaom.5c00060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep Learning-Optimized, Fabrication Error-Tolerant Photonic Crystal Nanobeam Cavities for Scalable On-Chip Diamond Quantum Systems
Cavity-enhanced diamond color center qubits can be initialized, manipulated, entangled, and read individually with high fidelity, which makes them ideal for large-scale, modular quantum computers, quantum networks, and distributed quantum sensing systems. However, diamond’s unique material properties pose significant challenges in manufacturing nanophotonic devices, leading to fabrication-induced structural imperfections and inaccuracies in defect implantation, which hinder reproducibility, degrade optical properties and compromise the spatial coupling of color centers to small mode-volume cavities. A cavity design tolerant to fabrication imperfections─such as surface roughness, sidewall slant, and nonoptimal emitter positioning─can improve coupling efficiency while simplifying fabrication. To address this challenge, a deep learning-based optimization methodology is developed to enhance the fabrication error tolerance of nanophotonic devices. Convolutional neural networks (CNNs) are applied to promising designs, such as L2 and fishbone nanobeam cavities, predicting Q-factors at least one-million times faster than traditional finite-difference time-domain (FDTD) simulations, enabling efficient optimization of complex, high-dimensional parameter spaces. The CNNs achieve prediction errors below 3.99% and correlation coefficients up to 0.988. Optimized structures demonstrate a 52% reduction in Q-factor degradation, achieving quality factors of 5 × 104 under real-world conditions and a 2-fold expansion in field distribution, enabling efficient coupling of nonoptimally positioned emitters. Compared to previous deep-learning optimization methods, this approach achieves twice the Q-factor performance in the presence of fabrication errors, significantly enhancing device robustness. Hence, this methodology enables scalable, high-yield manufacturing of robust nanophotonic devices, including the cavity-enhanced diamond quantum systems developed in this study.
期刊介绍:
ACS Applied Optical Materials is an international and interdisciplinary forum to publish original experimental and theoretical including simulation and modeling research in optical materials complementing the ACS Applied Materials portfolio. With a focus on innovative applications ACS Applied Optical Materials also complements and expands the scope of existing ACS publications that focus on fundamental aspects of the interaction between light and matter in materials science including ACS Photonics Macromolecules Journal of Physical Chemistry C ACS Nano and Nano Letters.The scope of ACS Applied Optical Materials includes high quality research of an applied nature that integrates knowledge in materials science chemistry physics optical science and engineering.