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Bayesian direct-binary-search algorithm for the efficient design of mosaic-based power splitters
| Title: | Bayesian direct-binary-search algorithm for the efficient design of mosaic-based power splitters |
| Authors: | Fujisawa, Takeshi Browse this author →KAKEN DB | | Saitoh, Kunimasa Browse this author →KAKEN DB |
| Issue Date: | 15-Apr-2021 |
| Publisher: | Optical Society of America |
| Journal Title: | OSA Continuum |
| Volume: | 4 |
| Issue: | 4 |
| Start Page: | 1258 |
| End Page: | 1270 |
| Publisher DOI: | 10.1364/OSAC.422116 |
| Abstract: | An efficient design method of mosaic-based power splitters based on Bayesian optimization technique is proposed. First, learning characteristics of Gaussian process (GP), which is one of the Bayesian inference techniques, is investigated to show its high regression performance. The transmission characteristics of 1x2 mosaic-based power splitters can be learned with an error of only 0.5%, which is comparable or better than simple ANN. Next, it is demonstrated that an efficient design of 1x2 mosaic-based power splitter with various splitting ratios is possible by using Bayesian optimization based on GP for selecting the next pixel. In the conventional direct-binary-search (DBS) design of mosaic-structure, the next pixel is chosen randomly. On the other hand, in the proposed method it is chosen based on the statistical information obtained by Bayesian inference. By accumulating the information of the transmission characteristics of the device obtained by electromagnetic (EM) simulation as training data, 70% reduction of the number of EM simulation compared with conventional DBS design is demonstrated. Furthermore, by using Bayesian optimization technique, it is shown that the device structure with better characteristics is obtained, compared with those obtained by conventional DBS design for the same number of EM simulation. There results indicate that the proposed method is useful for the design of mosaic-based devices. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement |
| Rights: | (c) 2021 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/81528 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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