Accuracy control and influence analysis of wavelength calibration for compact CCD spectrometer

On the base of hardware performance of spectrometer, wavelength calibration is the key process of further introducing errors. If the error level introduced in the solidification process of the calibration equation coefficients equals the pixel sampling error, then the wavelength measurement error in practical application includes two errors that comprises the pixel sampling error and the error introduced in the wavelength calibration equation calculation, which will enlarge and double the measurement error. Using the independently developed compact spectrometer, this paper aims to study the influence factors in wavelength calibration through data analysis, and then give an effective method to avoid error expanding. The FHWM of the adopted spectrometer is about 5 nm for a 100 μm slit, the wavelength interval of the pixels is about 0.45 nm, and the wavelength error of the hardware system is theoretically a half-pixel wavelength interval of 0.225 nm. The influence of the measurement noise and peak finding algorithm on calibration accuracy is analyzed emphatically, and the averaging noise reduction with multi-measurements combined with Gauss fitting peak finding method is proposed to improve calibration accuracy. Compared with traditional direct extremum method, the 2 times standard deviationof the fitting residual of calibration equation for the proposed method is about 0.1 nm, while the 2 times standard deviation of the fitting residual of calibration equation for traditional direct extremum method is 0.37 nm (two methods both use the 5th order polynomial as the calibration equation). Study reveals that the three key factors of error control in wavelength calibration are measurement noise control, peak finding algorithm and least squares adjustment. Through selecting appropriate algorithm parameters, the standard deviation of the fitting residual of wavelength calibration can be controlled at the level of about 1/10 pixel wavelength interval, which fully reflects the multi-pixel statistical advantage of Gauss fitting peak finding algorithm. © 2019, Science Press. All right reserved.