2018 Fiscal Year Final Research Report
Ghost imaging for detecting sub-nano defect
| Project/Area Number |
16H04375
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Measurement engineering
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| Research Institution | Osaka University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
高谷 裕浩 大阪大学, 工学研究科, 教授 (70243178)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Keywords | シングルピクセルイメージング / ゴーストイメージング / フォトンカウンティング / ディープラーニング / 微弱光イメージング / 欠陥検出 |
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| Outline of Final Research Achievements |
We describe a method for detecting a sub-nano defect by using the ghost imaging. In this study, we have developed two types system, such as high-sensitive imaging using arrival photon time and high-speed imaging with deep learning. By calculate spatial dispersion of correlation efficiency between illumination patterns and an arrival time of the photons, we have obtained fluorescence images using several hundred photons.
An improvement of imaging time for the ghost imaging is realized by using deep learning. We have applied a deep learning technique for reducing numbers of measurement. In the matter of a deep learning, the proposed method deals with a convolutional neural network. As a result, we have developed 60 times faster than the conventional GI. Additionally, we have observed a moving micro-particle with 0.08 sec.
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| Free Research Field |
光応用工学
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| Academic Significance and Societal Importance of the Research Achievements |
サブナノオーダの欠陥検出で必要となる微弱な光のイメージングを可能にする手法を提案した．ここでは，点型の光検出器でイメージングできるゴーストイメージングの感度および検出速度を向上させた．具体的には，フォトンカウンティング技術を用いてフォトンが到達する時間を利用したイメージング法を確立し数100フォトン以下でもイメージングが可能になるようにした．また，ディープラーニングを組み込むことで，5回という極めて少ない計測回数で測定を可能にした．
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