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13:00 - 13:30
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Manuscript ID. 0908
Paper No. 2022-FRI-S0901-I001
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Invited Speaker:
Pei-Kuen Wei
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Nano-Plasmonic Imaging Method for Microfluidic Digital Immunoassays
Pei-Kuen Wei, Academia Sinica (Taiwan)
Digital immunoassays with multiplexed capacity, high sensitivity, and high-throughput operation are urgently required in biomedical diagnosis, food safety and environmental monitoring. We developed gold nanostructures-based nanoplasmonic imaging method and applied it for digital immune analysis. Compared to current digital immunoassays that need fluorescent labeling and second antibody, the proposed nanoplasmonic imaging technology (NIT) can identify surface binding effect on single gold nanoparticle without any further labeling and reporting antibody. In this talk, the NIT method will be introduced and compared with other detection methods. The NIT will be used for detecting LSPR signals of gold nanoparticles and counted the antibody-antigen interactions for each single nanoparticles. The digital detections enhance the detection limit from ug/mL to pg/mL. It also takes advantages in the real application of complicated medium. Examples of detecting the spike protein of Covid-19 in salvia and pesticides in real samples will be presented
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13:30 - 13:45
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Award Candidate (Paper Competition)
Manuscript ID. 0106
Paper No. 2022-FRI-S0901-O001
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| Eknath Sarkar
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Optical and Electrical Crosstalk Reduction using Taper Shaped Deep Trench Isolation in Backside Illuminated CMOS Image Sensors
Eknath Sarkar, Yichen Ma, Yu-Chieh Lee, Chee Wee Liu, College of Electrical Engineering and Computer Science (Taiwan)
In this work, we have used finite difference time domain (FDTD) simulation to study the crosstalk behavior with the shape of deep trench isolation (DTI) of pixels arranged in a Bayer pattern. For blue illumination (λ=400 nm), the sum of optical and spectral crosstalk is lower for DTIs with values of Wratio > 1; whereas higher for green illumination (λ=550 nm). The increase in electrical crosstalk with higher Wratio values for blue illumination (λ=400 nm) is because of the higher generation of electron-hole pairs (EHPs) deep inside silicon (Si).
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13:45 - 14:00
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Award Candidate (Paper Competition)
Manuscript ID. 0224
Paper No. 2022-FRI-S0901-O002
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| Hsien-Yuan Chiu
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Silicon core based photonics crystal fiber applied for NH3 gas sensor
Hsien-Yuan Chiu, Ying-Tzu Lin, Hua-Chuan Weng, Yi-Lin Yu, Feng Chia University (Taiwan); Kimio Oguchi, National Taiwan University of Science and Technology (Taiwan); Wen-Fung Liu, Feng Chia University (Taiwan)
We proposed silicon core based photonics crystal fiber and applied on NH3 gas sensing. Au/graphene coating surrounding the air holes acted as a plasmonic active metal, and an average sensitivity was 0.0295 nm/ppm was obtained.
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14:00 - 14:15
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Award Candidate (Paper Competition)
Manuscript ID. 0289
Paper No. 2022-FRI-S0901-O003
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| Xin-Li Lee
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Deep Neural Network Acceleration for Optical Dispersion Estimation with FPGA Integration
Xin-Li Lee, Chia-Yuan Chang, Jui-Chi Chang, Shu-Yu Chang, National Cheng Kung University (Taiwan)
The frequency-resolved optical gating (FROG) is a powerful technique to analyze the optical dispersion in ultrafast laser. In this paper, we studied the fast optical dispersion estimation method based on deep neural network (DNN). The DNN-based dispersion estimation not only directly determine the group delay dispersion (GDD) from a single spectrogram, but also the computation time can achieve 4 times faster than standard principal component generalized projection algorithm (PCGPA). Different embedded computation platforms including Jetson Nano (GPU) and FPGA are discussed. And the FPGA shows the ability for integration with a real time applications.
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14:15 - 14:30
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Award Candidate (Paper Competition)
Manuscript ID. 0568
Paper No. 2022-FRI-S0901-O004
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| En-Shuo Lee
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Fiber Michelson Interferometer Formed by Pencil-shaped SMF for Liquid-level Sensing
En-Shuo Lee, Chin-Ping Yu, National Sun Yat-sen University (Taiwan)
A new liquid-level optical fiber sensor based on Michelson interferometer (MI) is proposed and demonstrated. The sensor is formed by splicing a pencil-shaped single-mode fiber with another single-mode fiber. The experimental results show that very high liquid-level sensing sensitivity of -41.4 pm/mm can be realized. We have also discussed its temperature properties.
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14:30 - 14:45
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Award Candidate (Paper Competition)
Manuscript ID. 0702
Paper No. 2022-FRI-S0901-O005
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| Wei-Shiuan Huang
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Shack-Hartmann Based Wavefront and Intensity Sensing via U-Net
Wei-Shiuan Huang, Feng-Chun Hsu, Chun-Yu Lin, College of Photonics (Taiwan); Chia-Yuan Chang, Department of Mechanical Engineering (Taiwan); Chia-Wei Hsu, Shean-Jen Chen, College of Photonics (Taiwan)
Deep learning has been recently applied to adaptive optics, makes it possible to quickly correct optical aberrations. Furthermore, the Shack-Hartmann wavefront sensor (SHWS) is an essential tool for wavefront sensing in adaptive optics system. In our research, we propose a deep learning method to reconstruct the wavefront, we integrate U-Net with SHWS. This approach is not only directly inferred the exact phase information from the images received by SHWS, but also the intensity of beam profile. Moreover, compared to the traditional method using of center of weight algorithm, the U-Net significantly reduces the time to compute the wavefront and intensity.
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14:45 - 15:00
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Award Candidate (Paper Competition)
Manuscript ID. 0754
Paper No. 2022-FRI-S0901-O006
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| Po-Chung Chen
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Using Diffraction Images of Micro-Polymer Probes Fabricated on Multimode Fiber Ends for Liquid Refractive Index Sensing
Po-Chung Chen, Ching-Hsu Chen, Fang-Wen Sheu, National Chiayi University (Taiwan)
This study firstly explores how to fabricate micro-polymer probes on the end section of graded index multimode fiber (core diameter of 62.5 μm) using UV lithography process technology. We have successfully fabricated various miniature polymer probes, and used the change of the interference ring separation of the output diffracted light after passing through solutions of different concentrations (refractive indices) to sense the change of the refractive index of the liquid.
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