Inverse design of an integrated-nanophotonics optical neural network

Qu, Yue; Zhu, Huanzheng; Shen, Yichen; Zhang, Jin; Tao, Chenning; Ghosh, Pintu; Qiu, Min

doi:10.1016/j.scib.2020.03.042

Cited by 63 publications

(29 citation statements)

References 41 publications

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“…In 2020, Y. Qu et al from Oregon State University, inspired by the NNM, proposed an integrated ONN framework based on optical scattering elements based on optical scattering unit [49], taking the network structure of coherent nanophotonic circuits as a prototype, which integrated optical interference unit and optical nonlinear unit. The core structure of the optical network framework is an integrated nano-photonic computing unit--Optical Scattering Unit OSU, which be comprised of a multi-mode Fig.…”

Section: Scattering Of Light To Realize Linear Operationmentioning

confidence: 99%

Research progress in optical neural networks: theory, applications and developments

et al. 2021

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With the advent of the era of big data, artificial intelligence has attracted continuous attention from all walks of life, and has been widely used in medical image analysis, molecular and material science, language recognition and other fields. As the basis of artificial intelligence, the research results of neural network are remarkable. However, due to the inherent defect that electrical signal is easily interfered and the processing speed is proportional to the energy loss, researchers have turned their attention to light, trying to build neural networks in the field of optics, making full use of the parallel processing ability of light to solve the problems of electronic neural networks. After continuous research and development, optical neural network has become the forefront of the world. Here, we mainly introduce the development of this field, summarize and compare some classical researches and algorithm theories, and look forward to the future of optical neural network.

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Section: Scattering Of Light To Realize Linear Operationmentioning

confidence: 99%

Research progress in optical neural networks: theory, applications and developments

et al. 2021

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“…[116], Copyright 2018 OSA; (c) Ref. [117], Copyright 2020 Elsevier; (d) Ref. [118], Copyright 2019 OSA of ONN, that is, photonic integrated circuits (i.e., built-in n-layer architecture in the sketch of Fig.…”

Section: Optical Interference Unitmentioning

confidence: 99%

“…For example, as displayed in Fig. 10 (c), Qu et al [117] introduced a novel integrated ONN framework based on optical scattering units (OSUs). In principle, resting on multimode interferences, OSUs could directly manipulate light intensity to run arbitrary matrix multiplications (see the sketch on the top panel of Fig.…”

Section: Optical Interference Unitmentioning

confidence: 99%

Intelligent designs in nanophotonics: from optimization towards inverse creation

Wang

Yan

et al. 2021

PhotoniX

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Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years. Inverse design of functional nanostructures is at the heart of this topic, in which artificial intelligence (AI) furnishes various optimization toolboxes to speed up prototyping of photonic layouts with enhanced performance. In this review, we offer a systemic view on recent advancements in nanophotonic components designed by intelligence algorithms, manifesting a development trend from performance optimizations towards inverse creations of novel designs. To illustrate interplays between two fields, AI and photonics, we take meta-atom spectral manipulation as a case study to introduce algorithm operational principles, and subsequently review their manifold usages among a set of popular meta-elements. As arranged from levels of individual optimized piece to practical system, we discuss algorithm-assisted nanophotonic designs to examine their mutual benefits. We further comment on a set of open questions including reasonable applications of advanced algorithms, expensive data issue, and algorithm benchmarking, etc. Overall, we envision mounting photonic-targeted methodologies to substantially push forward functional artificial meta-devices to profit both fields.

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“…It has been proved that the unitary matrix could be realized via all-optical devices [124,125] and the diagonal matrix could be achieved by attenuators. Shen et al cascaded programmable Mach-Zehnder interferometers (MZI) for different unitary matrix designs [126], while Qu et al utilized topology optimization algorithm to design the unitary matrix, which is much more compact than cascaded MZI [80]. In both of the above-mentioned ONN architectures, the optical weights matrices are connected to a personal computer for realizing nonlinear activation, as optics are linear in silicon platform when the intensity of input light is low.…”

Section: Layered Onnsmentioning

confidence: 99%

Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks

et al. 2021

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Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices with similar structure much faster than iterative optimization methods and are thus suitable in situations where piles of optical components are needed. Finally, the applications of inverse design methodology in optical neural networks are also discussed. This review intends to provide the readers with the suggestion for the most suitable design methodology for a specific scenario.

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Inverse design of an integrated-nanophotonics optical neural network

Cited by 63 publications

References 41 publications

Research progress in optical neural networks: theory, applications and developments

Research progress in optical neural networks: theory, applications and developments

Intelligent designs in nanophotonics: from optimization towards inverse creation

Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks

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