Ultra-Broadband Compact Polarization Beam Splitter Based on Asymmetric Etched Directional Coupler

Mao, Simei; Cheng, Lirong; Mu, Xin; Wu, Shoujun; Fu, H. Y.

doi:10.1364/cleopr.2020.c12h_1

Cited by 5 publications

(7 citation statements)

References 6 publications

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“…Other merits such as performance, compactness or fabrication feasibility may also be considered for different application scenarios. Based on the number of DOF, three types of structures are widely used for inverse design: empirical structure [21][22][23][24][25][26][27][28][29][30][31][32], QR-code like structure [33][34][35][36][37][38][39][40][41][42][43][44][45] and irregular structure [46][47][48][49][50][51][52][53][54][55][56][57][58]. An empirical structure making use of existing classical structures usually has only a few DOF.…”

Section: Inverse Design Schemes For Silicon Photonicsmentioning

confidence: 99%

“…We take reference [25] for example to illustrate utilizing GA to optimize an optical device with empirical structure. A broadband PBS based on directional coupler and subwavelength gratings (SWGs) structure is designed as shown in Figure 2b.…”

Section: Optimization Of Empirical Structuresmentioning

confidence: 99%

“…According to the global best particle and the local best particle at their current iteration, the velocity of each particle is updated. With updated velocities, the new population We take reference [25] for example to illustrate utilizing GA to optimize an optical device with empirical structure. A broadband PBS based on directional coupler and subwavelength gratings (SWGs) structure is designed as shown in Figure 2b.…”

Section: Optimization Of Empirical Structuresmentioning

confidence: 99%

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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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Section: Inverse Design Schemes For Silicon Photonicsmentioning

confidence: 99%

Section: Optimization Of Empirical Structuresmentioning

confidence: 99%

Section: Optimization Of Empirical Structuresmentioning

confidence: 99%

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

et al. 2021

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“…Using 400 nm thick Si3N4 as the scope of our work, we demonstrate pixel-based photonic splitter designs with splitting ratio (r) ranging from r = 0.25 to r = 10.0, and insertion loss (IL) of 0.4𝑑𝐵 < 𝐼𝐿 < 1.2𝑑𝐵. Specifically, we show splitter with 1:3 splitting ratio can be achieved with transmission efficiency of ~85% and device footprint smaller than 16m 2 . The proposed method demonstrates significantly faster convergence times compared to genetic algorithms and the direct binary search method.…”

Section: Introductionmentioning

confidence: 97%

“…While iterative algorithms can be used to optimize the device geometrical parameters for targeted optical performances, establishing explicit functions to define the connections between geometry parameters and optical performance proves to be highly challenging. Alternatively, gradient-free algorithms such as Direct Binary Search (DBS) [1] and Genetic Algorithm (GA) [2] can be employed to obtained targeted device performances, yet they typically suffer from slow convergence rates, where DBS might take up to 140 hours, and GA up to 96 hours to reach convergence. Another approach worth mentioning is gradient-based algorithm, such as topology optimization (TO) [3] and the adjoint method [4], which typically require huge computational demands.…”

Section: Introductionmentioning

confidence: 99%

Photonic power splitter design based on deep learning and gradient descent method

Kong,

Tobing,

Zhang

2024

Photonic and Phononic Properties of Engineered Nanostructures XIV

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Increasing demand for high density and broadband photonic integrated circuit (PIC) has motivated designs for photonic devices with high performance and compact footprint. However, the number of parameters in traditional photonic device designs is limited by the working principles for such devices, which often results in a perceived trade-off among device performances, such as bandwidth, efficiency, and footprint. Nonlinear optimizations such as direct binary search (DBS) and genetic algorithms (GA) have been explored in photonic designs, yet they have drawbacks such as slow convergence time in the range of 96-140 hours. By contrast, designs based on deep learning model relates device performances (output) and device parameters (inputs) via data-driven methodology, which enables arbitrarily large number of design parameter space that may overcome the perceived trade-off in traditional photonic designs. This work proposes a design methodology based on a combination of deep learning model and gradient descent method for photonic power splitter with arbitrary splitting ratio. Using pixel-based device geometry, a deep learning model relating the geometric parameters and device spectral performance is first established. Afterwards, a figure of merit based on a target splitting ratio is optimized through gradient descent method to yield the corresponding pixel-based device geometry. We demonstrate this method in the design of photonic power splitter with splitting ratio from 0.25 to 10, with insertion loss between 0.5dB to 1.16dB, and device size smaller than 16 m 2 . In comparison with the genetic algorithm and direct binary search method, our proposed method is much faster in terms of convergence time.

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Flexible guided-wave manipulation using phase-gradient dielectric metasurface antenna array

et al. 2022

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Ultra-Broadband Compact Polarization Beam Splitter Based on Asymmetric Etched Directional Coupler

Abstract: We propose an ultra-broadband polarization beam splitter with 4.6 pm coupling length. Extinction ratio over 10 dB and insertion loss less than 1 dB are achieved over bandwidth of 420 nm centred at 1460 nm.

Cited by 5 publications

References 6 publications

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

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

Photonic power splitter design based on deep learning and gradient descent method

Flexible guided-wave manipulation using phase-gradient dielectric metasurface antenna array

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