Tackling Photonic Inverse Design with Machine Learning

Liu, Zhaocheng; Zhu, Dayu; Raju, Lakshmi; Cai, Wenshan

doi:10.1002/advs.202002923

Cited by 105 publications

(74 citation statements)

References 116 publications

(97 reference statements)

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“…This problem is compounded for design problems, in which a geometry is sought to enable a desired photonic transfer function. In such cases, many forward simulations are required along with an optimization strategy to find the optimal solution [266]. Machine learning tools greatly increase the speed of forward design, and thereby also enable rapid inverse design when coupled with techniques such as adjoint methods.…”

Section: Statusmentioning

confidence: 99%

2022 Roadmap on integrated quantum photonics

et al. 2022

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Integrated photonics will play a key role in quantum systems as they grow from few-qubit prototypes to tens of thousands of qubits. The underlying optical quantum technologies can only be realized through the integration of these components onto quantum photonic integrated circuits (QPICs) with accompanying electronics. In the last decade, remarkable advances in quantum photonic integration have enabled table-top experiments to be scaled down to prototype chips with improvements in efficiency, robustness, and key performance metrics. These advances have enabled integrated quantum photonic technologies combining up to 650 optical and electrical components onto a single chip that are capable of programmable quantum information processing, chip-to-chip networking, hybrid quantum system integration, and high-speed communications. In this roadmap article, we highlight the status, current and future challenges, and emerging technologies in several key research areas in integrated quantum photonics, including photonic platforms, quantum and classical light sources, quantum frequency conversion, integrated detectors, and applications in computing, communications, and sensing. With advances in materials, photonic design architectures, fabrication and integration processes, packaging, and testing and benchmarking, in the next decade we can expect a transition from single- and few-function prototypes to large-scale integration of multi-functional and reconfigurable devices that will have a transformative impact on quantum information science and engineering.

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Section: Statusmentioning

confidence: 99%

2022 Roadmap on integrated quantum photonics

et al. 2022

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“…We expect that many previously developed photonic biosensing platforms can be redesigned to operate in the probe-cleavage detection regime and provide multiplexed capabilities. The recently developed toolbox of the inverse design and machine-learning techniques [185,[191][192][193][194][195] can be utilized to find the optimum sensor configuration (i.e., a photonic device platform, readout mechanism, type and surface coverage of the nanopar-ticle probes, etc.) that balances the sensor ease of use, detection time, LOD, and the dynamic range requirements.…”

Section: Discussionmentioning

confidence: 99%

Roadmap on Universal Photonic Biosensors for Real-Time Detection of Emerging Pathogens

et al. 2021

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The COVID-19 pandemic has made it abundantly clear that the state-of-the-art biosensors may not be adequate for providing a tool for rapid mass testing and population screening in response to newly emerging pathogens. The main limitations of the conventional techniques are their dependency on virus-specific receptors and reagents that need to be custom-developed for each recently-emerged pathogen, the time required for this development as well as for sample preparation and detection, the need for biological amplification, which can increase false positive outcomes, and the cost and size of the necessary equipment. Thus, new platform technologies that can be readily modified as soon as new pathogens are detected, sequenced, and characterized are needed to enable rapid deployment and mass distribution of biosensors. This need can be addressed by the development of adaptive, multiplexed, and affordable sensing technologies that can avoid the conventional biological amplification step, make use of the optical and/or electrical signal amplification, and shorten both the preliminary development and the point-of-care testing time frames. We provide a comparative review of the existing and emergent photonic biosensing techniques by matching them to the above criteria and capabilities of preventing the spread of the next global pandemic.

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“…Many of the works above have been highlighted and presented individually in more detail in recent related review articles, [ 155–160 ] and we show a few representative examples in Figure . A more complete survey of DL forward modeling in recent AEM literature is given in Table 2 , categorized using the AEM classes in Section 3 and DNN architecture (discussed below), and noting that some systems may exist adjacent to these strict classifications (e.g., nanophotonic devices).…”

Section: Forward Modeling Of Aemsmentioning

confidence: 99%

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Khatib

Ren

Malof

et al. 2021

Adv Funct Materials

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Deep neural networks (DNNs) are empirically derived systems that have transformed traditional research methods, and are driving scientific discovery. Artificial electromagnetic materials (AEMs)—including electromagnetic metamaterials, photonic crystals, and plasmonics—are research fields where DNN results valorize the data driven approach; especially in cases where conventional methods have failed. In view of the great potential of deep learning for the future of artificial electromagnetic materials research, the status of the field with a focus on recent advances, key limitations, and future directions is reviewed. Strategies, guidance, evaluation, and limits of using deep networks for both forward and inverse AEM problems are presented.

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Tackling Photonic Inverse Design with Machine Learning

Cited by 105 publications

References 116 publications

2022 Roadmap on integrated quantum photonics

2022 Roadmap on integrated quantum photonics

Roadmap on Universal Photonic Biosensors for Real-Time Detection of Emerging Pathogens

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

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