APD optimal bias voltage compensation method based on machine learning

Cao, Yihan; Bu, Xiongzhu; Xu, Miaomiao; Han, Wei

doi:10.1016/j.isatra.2019.08.016

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Within measurement electronics, photodetectors are a large source of error. In addition to thermal noise and shot noise [14][15][16], the most significant impact is the deviation caused by changes in photoelectronic delays [30,31]. Changes in light intensity will cause the internal bias voltage of the photodiode to fluctuate, thereby affecting the transit time of photoelectrons [32].…”

Section: Resolution Of Heterodyne Interferometermentioning

confidence: 99%

“…Firstly, in the process of transforming the interference signal from optical to electrical, the photodetector will introduce noise, resulting in a decrease in signal quality. At present, there are numerous studies that have been conducted to obtain high signal-to-noise ratio signals through auto-self-gain [15] and noise optimization design [16]. Secondly, different phase calculation principles will also result in differences in resolution.…”

Section: Introductionmentioning

confidence: 99%

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A Picometre-Level Resolution Test Method without Nonlinearity for Heterodyne Interferometer Measurement Electronics

Sun,

Li,

Xing

et al. 2024

Photonics

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The wide application of displacement measurement in high-precision equipment production and high-precision metrology is placing increasing pressure on the resolution of heterodyne interferometers. However, as the core component of an interferometer, since measurement electronics includes the cross-physical process of photoelectric conversion, its resolution is rarely evaluated, either on an individual level or as a whole. Therefore, in this paper, we propose a picometer resolution test method for measurement electronics, that uses intensity modulation signals based on an AOM to replace the beat frequency interference signals, and an ordinary commercial guide rail to equivalently generate the pm-level displacement of the heterodyne interferometer under laboratory conditions. Based on the detailed analysis of the type of noise in the test device, the correlation between the light intensity and the nonlinear error was established, and nonlinearity was suppressed to 10% of the original level. Furthermore, this test method allows one to perform a 0.1 mrad phase step test at 1 MHz signal frequency, equivalent to a 2.5 pm resolution test in a double-pass heterodyne interferometer. Simultaneously, it can be directly applied to the resolution test for measurement electronics with a center frequency in the range of 1 MHz to 20 MHz.

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Section: Resolution Of Heterodyne Interferometermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Picometre-Level Resolution Test Method without Nonlinearity for Heterodyne Interferometer Measurement Electronics

Sun,

Li,

Xing

et al. 2024

Photonics

View full text Add to dashboard Cite

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“…Among them, Liu, W. J. et al evaluated the operating state of APD in visible communication by detecting the waveform response characteristics to perform APD gain adjustment [5]. Y. H. Cao performed voltage compensation by determining the A/D sampling data of the APD received signal by machine learning method [6]. This method does not need to consider all environmental factors that affect APD performance.…”

Section: Introductionmentioning

confidence: 99%

Optimal voltage compensation method for focal plane avalanche detector based on improved random forest

Chen

Yang

et al. 2023

J. Phys.: Conf. Ser.

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Working distance and background radiation greatly affect the signal-to-noise ratio of avalanche photodiode (APD) in the lidar detection system. The traditional method cannot adapt to a complex environment by offline compensation or pre-compensation according to the influence factors of the external environment. In this paper, an avalanche photodiode voltage compensation method based on the improved random forest is designed. Firstly, the distance image data is de-noised. Then the weight of each decision tree in the random forest was changed to improve the classification performance. The particle swarm optimization (PSO) algorithm was used to search for the optimal combination of parameters affecting classification accuracy and performance. Finally, the improved random forest algorithm is used to judge the current working state of APD at different distances, compensate for the bias voltage, and make APD work in the optimal state. The proposed method is compared with the k-nearest neighbor, support vector machine, and other commonly used classification algorithms, and the results verify the effectiveness of the proposed method.

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