Deep Neural Networks for Designing Integrated Photonics

Kojima, K.; Tahersima, Mohammad H.; Koike‐Akino, Toshiaki; Jha, Devesh K.; Tang, Yingheng; Wang, Ye; Parsons, Kieran; Sang, Fengqiao; Klamkin, Jonathan

doi:10.1364/ofc.2020.th1a.6

Cited by 3 publications

(5 citation statements)

References 5 publications

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“…Inspired by the previous work, [171] Tang et al reported a series of remarkable achievements in applying CVAE to design digital beam splitters. [172][173][174][175][176] Among them, the most detailed illustration is given in the literature. [172] This paper refined their method several times, resulting in the design of SOI-based power splitters with an ultra-compact footprint of 2.25 μm × 2.25 μm and ≈90% transmission efficiency over 1250-1800 nm wavelengths.…”

Section: Variational Autoencoder (Vae)mentioning

confidence: 99%

“…Another example of VAE combined with the ADJ algorithm for the inverse design of a nanophotonic power splitter was reported in Ref. [178] Besides power splitters, [173][174][175][176]178] VAE has more recently achieved notable success in other silicon photonic devices, like digital multimode interference waveguides [179] and the Starshot lightsail, [180] see Figure 26a-d.…”

Section: Variational Autoencoder (Vae)mentioning

confidence: 99%

Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

mentioning

confidence: 99%

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The Intelligent Design of Silicon Photonic Devices

Li,

Zhou,

Qiu

et al. 2024

Advanced Optical Materials

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Photonic devices based on silicon waveguides are essential to versatile high‐performance and low‐cost photonic integrated systems. Extremely complex silicon photonic devices with hundreds or even thousands of degrees of freedom (DOF) are successfully designed and manufactured based on recent advances in data science and nanofabrication technology. At this level, conventional forward‐reasoning may no longer be suitable for designing high‐performance silicon photonic devices with novel functionalities since the light‐matter interaction is complex and non‐intuitive. Therefore, the timely development of sub‐wavelength silicon photonic devices that can precisely mold the flow of light is a critical and urgent issue requiring joint engineering and scientific efforts. In this paper, an inverse design strategy based on heuristic and gradient descendant algorithms, enabling the realization of large‐scale integrated devices is first introduced. Subsequently, the burgeoning deep learning technology, which offers a promising direction for the automation design of silicon photonics with a data‐driven approach, is discussed. Finally, the obstacles and prospects in this emerging research direction are revealed. Detail discussions from multiple perspectives are provided. This review aims to provide general guidance and a comprehensive reference for scientists developing photonic integrated systems.

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Section: Variational Autoencoder (Vae)mentioning

confidence: 99%

Section: Variational Autoencoder (Vae)mentioning

confidence: 99%

Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

mentioning

confidence: 99%

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The Intelligent Design of Silicon Photonic Devices

Li,

Zhou,

Qiu

et al. 2024

Advanced Optical Materials

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“…Models and simulations based on ML approaches can, for instance, quickly narrow down the parameter space of specific variables involved in fabrication processes [18,181,182]. They can also be used to control, tweak or even design ad hoc properties of materials [131], heterostructures [9,132] and devices [19, 136,137], again while being suitable for fabrication strategies that require large-scale, fast and automated production.…”

Section: Color Center Synthesis and Stabilitymentioning

confidence: 99%

Machine and quantum learning for diamond-based quantum applications

Stone

Bradac²

2023

Mater. Quantum. Technol.

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In recent years, machine and quantum learning have gained considerable momentum sustained by growth in computational power and data availability and have shown exceptional aptness for solving recognition- and classification-type problems, as well as problems that require complex, strategic planning. In this work, we discuss and analyze the role machine and quantum learning are playing in the development of diamond-based quantum technologies. This matters as diamond and its optically-addressable spin defects are becoming prime hardware candidates for solid state-based applications in quantum information, computing and metrology. Through a selected number of demonstrations, we show that machine and quantum learning are leading to both practical and fundamental improvements in measurement speed and accuracy. This is crucial for quantum applications, especially for those where coherence time and signal-to-noise ratio are scarce resources. We summarize some of the most prominent machine and quantum learning approaches that have been conducive to the presented advances and discuss their potential for proposed and future quantum applications.

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