COLLABORATIVE LEARNING APPLIED TO TRAINING A META-OPTIMIZING FUNCTION TO COMPUTE PARAMETERS FOR DESIGN HOUSE FUNCTIONS

    公开(公告)号:US20230133722A1

    公开(公告)日:2023-05-04

    申请号:US17515368

    申请日:2021-10-29

    Abstract: Systems and methods are provided for creating and sharing knowledge among design houses. In particular, examples of the presently disclosed technology leverage the concepts of meta-optimizing and collaborative learning to reduce the computational burden shouldered by individual design houses using inverse design techniques to find optimal designs in a manner which protects intellectual property sensitive information. Examples may share versions of a central meta-optimizer (i.e. local meta-optimizers) among design houses targeting different (but related) design tasks. A local meta-optimizer can be trained to indirectly optimize a design task by computing hyper-parameters for a design house's private optimization function. The private optimization function may be using inverse design techniques to find an optimal design for a design task. This may correspond to finding a global minimum of a cost function using gradient descent techniques or more advanced global optimization techniques.

    ETCH VARIATION TOLERANT DIRECTIONAL COUPLERS

    公开(公告)号:US20210373241A1

    公开(公告)日:2021-12-02

    申请号:US16883969

    申请日:2020-05-26

    Abstract: Embodiments of the present disclosure provide etch-variation tolerant optical coupling components and processes for making the same. An etch-variation tolerant geometry is determined for at least one waveguide of an optical coupling component (e.g., a directional coupler). The geometry is optimized such that each fabricated instance of an optical component design with the etch-variation tolerant geometry has substantially the same coupling ratio at any etch depth between a shallow etch depth and a deep etch depth.

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