Real-Time Classification of Optical Devices Using Rotating Linearly Polarized Light in a Sagnac Interferometer

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Rapeepan Kaewon
Jirasak Wongbongkotpaisan

Abstract

This study looks at how to create perfectly rotating linearly polarized light using phase-shifting methods in a Sagnac interferometer, aiming to categorize optical devices. Theoretical analysis is conducted using Jones calculus, which provides a framework for understanding the propagation and phase shifting of linear light. Experimental results from the Sagnac interferometer show interference fringes that align with predictions from mathematical simulations. The experimental observations are validated through comparisons with Python-based simulations, ensuring the accuracy of the rotating polarized light characteristics. Additionally, Convolutional Neural Network (CNN) techniques are employed to analyze and verify the interference fringes, further confirming the consistency of the results with Jones calculus theory. This work demonstrates the potential for applying these methods in real-time, non-destructive optical measurements for the inspection and classification of materials such as polarizers and Half Wave Plates (HWPs), advancing the field of optical device characterization.

Article Details

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Research Article

References

R. Kaewon, A. Bhatranand, Y. Jiraraksopakun, E. Siwapornsathain, C. Pawong, and R. Chitaree, “Generation of the rotating linearly polarized light using the triangular cyclic interferometer,” in Proc. 29th Int. Tech. Conf. Circuit/Syst. Comput. Commun. (ITC-CSCC), Phuket, Thailand, 2014, pp. 546–549.

R. Keawon, C. Pawong, A. Bhatranand, and R. Chitaree, “Fast and effective method to distinguish the polarizing components using a polarizing triangular cyclic interferometer,” in Proc. Asia Commun. Photon. Conf. (ACP), Shanghai, China, 2014, pp. 1–3.

R. Kaewon, C. Pawong, A. Bhatranand, and R. Chitaree, “Polarizing triangular cyclic interferometer for characterizing optical samples with birefringent properties,” in Proc. SPIE – Int. Conf. Photon. Solutions, Prachuap Khiri Khan, Thailand, 2015, Art. no. 96590L, doi: 10.1117/12.2195890.

S. Sarkar, N. Ghosh, S. Chakraborty, and K. Bhattacharya, “Self-referenced rectangular path cyclic interferometer with polarization phase shifting,” Appl. Optics., vol. 51, no. 1, pp. 126–132, 2012.

C. Pawong, R. Chitaree, and C. Soankwan, “The rotating linearly polarized light from a polarizing Mach–Zehnder interferometer: Production and applications,” Opt. Laser Technol., vol. 43, no. 3, pp. 461–468, 2011.

C. Pawong, R. Chitaree, and C. Soankwan, “Investigation of the use of rotating linearly polarized light for characterizing SiO2 thin-film on Si substrate,” in Proc. Asia Commun. Photon. Conf. Exhib. (ACP), Shanghai, China, 2011, pp. 1–8, doi: 10.1117/12.904420.

B. Kanseri and H. C. Kandpal, “Mathematical formulation for verification of the Fresnel and Arago interference laws using a Mach–Zehnder interferometer,” Optik, vol. 121, no. 11, pp. 1019–1026, 2010.

A. Usman, Y. Jiraraksopakun, R. Kaewon, C. Pawong, and A. Bhatranand, “The comparison of multi-stepping algorithms for real-time thickness measurement of transparent thin films using polarization settings,” Laser Phys., vol. 32, no. 12, 2022, Art. no. 125401.

A. Usman, A. Bhatranand, Y. Jiraraksopakun, R. Kaewon, and C. Pawong, “Real-time double-layer thin film thickness measurements using modified Sagnac interferometer with polarization phase shifting approach,” Photonics., vol. 8, no. 12, 2021, Art. no. 529, doi: 10.3390/photonics8120529.

B. Mahesh, “Machine learning algorithms – A review,” Int. J. Sci. Res., vol. 9, no. 1, pp. 381–386, 2020.

J. D. Kothari, “A case study of image classification based on deep learning using TensorFlow,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 6, no. 4, pp. 3888–3892, 2018.

I. Castiglioni et al., “AI applications to medical images: From machine learning to deep learning,” Physica Medica., vol. 83, pp. 9–24, 2021.

A. Serag et al., “Translational AI and deep learning in diagnostic pathology,” Front. Med., vol. 6, 2019, doi: 10.3389/fmed.2019.00185.

R. Kaewon, K. Kittawee, J. Wongbongkotpaisan, and A. Bhatranand, “Validation of perfectly rotating polarized light using Sagnac interferometry: A comparison of Python simulations and machine learning technique,” in Proc. Int. Conf. Photon. Solutions (ICPS), Bangkok, Thailand, 2024, Art. no. 135180M, doi: 10.1117/12.3058666.