Compensation of non-ideal beam splitter polarization distortion effect in Michelson interferometer

Liu, Yeng Cheng; Lo, Yu Lung; Liao, Chia Chi

doi:10.1016/j.optcom.2015.09.099

Cited by 12 publications

(8 citation statements)

References 16 publications

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“…Johs and He [11] mentioned that a NPBS induces polarization distortion. Liu et al [12] measured the Mueller matrices of a NPBS in transmission and reflection for compensating the polarization distortion. Therefore, the polarization effect of the NPBS cannot be neglected.…”

Section: Experiments Resultsmentioning

confidence: 99%

Measurement of ellipsometric data and surface orientations by modulated circular polarized light / Messung von ellipsometrischen Daten und Oberflächenorientierungen durch moduliertes zirkular polarisiertes Licht

Chen

Hartrumpf

Längle

et al. 2019

Tm - Technisches Messen

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Ellipsometry is a widely used optical method for the characterization of materials and thin films. However, only flat or nearly flat surfaces can be measured since small deviations of the angle of incidence might lead to significant experimental errors. In order to overcome the geometrical limitations of ellipsometry, the retroreflex ellipsometry developed at Fraunhofer IOSB is used. Based on this configuration, a new illumination method is proposed. When the sample is illuminated by modulated circular polarized light (LCP and RCP), the ellipsometric parameters (Ψ, Δ) and the tilted angle (Φ) can be determined directly. Combined with reflectance measurement or prior knowledge of optical properties of samples, the surface orientation can be obtained.

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Section: Experiments Resultsmentioning

confidence: 99%

Measurement of ellipsometric data and surface orientations by modulated circular polarized light / Messung von ellipsometrischen Daten und Oberflächenorientierungen durch moduliertes zirkular polarisiertes Licht

Chen

Hartrumpf

Längle

et al. 2019

Tm - Technisches Messen

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show abstract

“…For symmetric BeS, r 00 = r 11 = r and t 01 = t 10 = t. Additionally, for ideal 3-dB BeS, r = t = 1 √ 2 . However, under uncertainties, r and t will deviate from 1 √ 2 ; this results in unbalanced and imperfect BeS [24], [40]. Unlike PhS, BeS are passive devices and once fabricated, we cannot actively change their r and t values during IPNN training (e.g., in a passive directional coupler).…”

Section: A Component-level: Phase Shifters and Beam Splittersmentioning

confidence: 99%

Characterizing Coherent Integrated Photonic Neural Networks under Imperfections

Banerjee¹,

Nikdast²,

Chakrabarty³

2022

Preprint

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Integrated photonic neural networks (IPNNs) are emerging as promising successors to conventional electronic AI accelerators as they offer substantial improvements in computing speed and energy efficiency. In particular, coherent IPNNs use arrays of Mach-Zehnder interferometers (MZIs) for unitary transformations to perform energy-efficient matrix-vector multiplication. However, the underlying MZI devices in IPNNs are susceptible to uncertainties stemming from optical lithographic variations and thermal crosstalk and can experience imprecisions due to non-uniform MZI insertion loss and quantization errors due to low-precision encoding in the tuned phase angles. In this paper, we, for the first time, systematically characterize the impact of such uncertainties and imprecisions (together referred to as imperfections) in IPNNs using a bottom-up approach. We show that their impact on IPNN accuracy can vary widely based on the tuned parameters (e.g., phase angles) of the affected components, their physical location, and the nature and distribution of the imperfections. To improve reliability measures, we identify critical IPNN building blocks that, under imperfections, can lead to catastrophic degradation in the classification accuracy. We show that under multiple simultaneous imperfections, the IPNN inferencing accuracy can degrade by up to 46%, even when the imperfection parameters are restricted within a small range. Our results also indicate that the inferencing accuracy is sensitive to imperfections affecting the MZIs in the linear layers next to the input layer of the IPNN.

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“…However, under random FPVs, r and t will deviate from 1 √ 2 ; this results in unbalanced and imperfect BeS [12], [13]. Unlike PhS, BeS are passive devices and once fabricated, we cannot actively change their r and t values during SPNN training.…”

Section: A Component-level: Phase Shifters and Beam Splittersmentioning

confidence: 99%

Modeling Silicon-Photonic Neural Networks under Uncertainties

Banerjee¹,

Nikdast²,

Chakrabarty³

2021

2021 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Silicon-photonic neural networks (SPNNs) offer substantial improvements in computing speed and energy efficiency compared to their digital electronic counterparts. However, the energy efficiency and accuracy of SPNNs are highly impacted by uncertainties that arise from fabrication-process and thermal variations. In this paper, we present the first comprehensive and hierarchical study on the impact of random uncertainties on the classification accuracy of a Mach-Zehnder Interferometer (MZI)based SPNN. We show that such impact can vary based on both the location and characteristics (e.g., tuned phase angles) of a non-ideal silicon-photonic device. Simulation results show that in an SPNN with two hidden layers and 1374 tunable-thermalphase shifters, random uncertainties even in mature fabrication processes can lead to a catastrophic 70% accuracy loss.

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Compensation of non-ideal beam splitter polarization distortion effect in Michelson interferometer

Cited by 12 publications

References 16 publications

Measurement of ellipsometric data and surface orientations by modulated circular polarized light / Messung von ellipsometrischen Daten und Oberflächenorientierungen durch moduliertes zirkular polarisiertes Licht

Measurement of ellipsometric data and surface orientations by modulated circular polarized light / Messung von ellipsometrischen Daten und Oberflächenorientierungen durch moduliertes zirkular polarisiertes Licht

Characterizing Coherent Integrated Photonic Neural Networks under Imperfections

Modeling Silicon-Photonic Neural Networks under Uncertainties

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