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APD optimal bias voltage compensation method based on machine learning.

Yihan Cao1, Xiongzhu Bu1, Miaomiao Xu1

  • 1Nanjing University of Science and Technology, School of Mechanical Engineering, 200 Xiaolingwei, Nanjing, 210094, China.

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|August 12, 2019
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Summary
This summary is machine-generated.

Machine learning optimizes avalanche photodiode (APD) bias voltage by accurately assessing laser and APD states. This improves signal-to-noise ratio in optical detection systems, offering a promising new compensation method.

Keywords:
APDBias voltage compensationDichotomy compensationMachine learning

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Area of Science:

  • Optical Engineering
  • Machine Learning Applications
  • Sensor Technology

Background:

  • Signal-to-noise ratio (SNR) in avalanche photodiodes (APDs) is sensitive to background radiation and temperature.
  • Existing compensation methods may not optimally adapt to dynamic environmental conditions.

Purpose of the Study:

  • To develop a machine learning-based method for optimal avalanche photodiode (APD) bias voltage compensation.
  • To enhance the performance of optical detection systems by maintaining APDs in their optimal working state.

Main Methods:

  • Designed a machine learning model to accurately determine laser emission and APD working states.
  • Employed cross-verification for state assessment accuracy.
  • Optimized model by reducing input variables to improve prediction speed.
  • Utilized road detection as an application scenario for validation.

Main Results:

  • Achieved 100% accuracy in judging laser emission state and 99.3% accuracy in judging APD working state.
  • Demonstrated improved prediction speed through model optimization.
  • Showcased the method's effectiveness in a road detection application.

Conclusions:

  • The proposed machine learning-based APD optimal bias voltage compensation method is effective.
  • This approach offers a novel and promising solution for improving optical detection systems.
  • The method enhances APD performance under varying conditions.