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

Due to the high accuracy and fast response, measurement systems based on four-quadrant detectors (4QDs) are widely used. There is a non-linear relationship between the output signal offset (OSO) of the 4QD and the actual spot position, resulting in limited measurement accuracy. Existing methods improve detection accuracy by collecting large amounts of data and approximating the OSO curve. On one hand, they require much difficult-to-obtain real data; on the other hand, the accuracy of the fit using specific functions is limited. To address this issue, this paper proposes a neural-network-based method for improving the measurement accuracy of 4QDs. Compared to existing methods, the proposed method significantly improves measurement accuracy with a small amount of real data. To obtain sufficient data to train the neural network, we first propose a method for generating large amounts of high-precision simulation data. Then, specifically for the 4QD-based measurement system, we construct a backpropagation neural network. Finally, based on a large amount of simulation data and a small amount of real data, we design a new training strategy to train a high-precision measurement network. The experimental results show that the proposed method can significantly improve measurement accuracy with less real data and has extensive application value.