Neural-network-based method for improving measurement accuracy of four-quadrant detectors

Area of Science:

• Optoelectronics
• Measurement Systems
• Artificial Intelligence

Background:

• Four-quadrant detectors (4QDs) offer high accuracy and fast response in measurement systems.
• A non-linear relationship between output signal offset and spot position limits 4QD measurement accuracy.
• Current methods rely on extensive real data and function fitting, which have limitations.

Purpose of the Study:

• To propose a novel neural network-based method for improving the measurement accuracy of 4QDs.
• To address the limitations of existing methods in terms of data requirements and accuracy.
• To develop a high-precision measurement network for 4QD systems.

Main Methods:

• Generation of high-precision simulation data to train the neural network.
• Construction of a backpropagation neural network specifically for 4QD measurement systems.
• A novel training strategy utilizing both simulated and limited real data.

Main Results:

• Significant improvement in measurement accuracy for 4QDs.
• Reduced need for large amounts of difficult-to-obtain real data.
• Demonstrated extensive application value in measurement systems.

Conclusions:

• The proposed neural network method effectively enhances 4QD measurement accuracy.
• The approach requires less real data compared to traditional methods.
• This technique offers a valuable solution for improving precision in 4QD-based systems.