Adaptive error compensation of heterodyne laser interferometer using DFNN

As an ultra-precision measurement system the heterodyne laser interferometer plays an important role in semiconductor industry. However the errors of environment and nonlinearity which are caused by air refraction and frequency-mixing separately reduce the accuracy of displacement measurement. In this paper we propose a DFNN(data fusion and neural network) method for error compensation. As a hybrid method of data fusion and neural network, DFNN method reduces the environmental and nonlinear error simultaneously. The effectiveness of the proposed error compensation method is proved through experimental results.