Adaptive Nonlinearity Compensation in Laser Interferometer using Neural Network

신경망 회로를 이용한 레이저 간섭계의 적응형 오차보정

In the semiconductor manufacturing industry, the heterodyne laser interferometer plays as an ultra-precise measurement system. However, the heterodyne laser interferometer has some unwanted nonlinearity error which is caused from frequency-mixing. This is an obstacle to improve the measurement accuracy in nanometer scale. In this paper we propose a compensation algorithm based on RLS(recursive least square) method and artificial intelligence method, which reduce the nonlinearity error in the heterodyne laser interferometer. With the capacitance displacement sensor we get a reference signal which can be transformed into the intensity domain. Using the back-propagation Neural Network method, we train the network to track the reference signal. Through some experiments, we demonstrate the effectiveness of the proposed algorithm in measurement accuracy.