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.

    STAGED NEURAL NETWORKS FOR SOLVING NP HARD/COMPLETE PROBLEMS

    公开(公告)号:US20200242448A1

    公开(公告)日:2020-07-30

    申请号:US16261398

    申请日:2019-01-29

    Abstract: Staged neural networks and methods therefor are provided for solving NP hard/complete problems. In some embodiments, the methods include identifying a plurality of second NP hard/complete problems, wherein each of the second NP hard/complete problems is similar to the first NP hard/complete problem; identifying solutions to the second NP hard/complete problems; training a deep neural network with the second NP hard/complete problems and the solutions; providing the first NP hard/complete problem to the trained deep neural network, wherein the trained deep neural network generates a preliminary solution to the first NP hard/complete problem; and providing the preliminary solution to a recursive neural network configured to execute an energy minimization search, wherein the recursive neural network generates a final solution to the problem based on the preliminary solution.

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