An apparatus and method for monitoring a stability of a spectrum are provided. The apparatus for monitoring stability of a spectrum includes a spectroscope configured to measure a spectrum of a sample and a processor configured to calculate a similarity change index of the measured spectrum and to determine the stability of the measured spectrum by analyzing the calculated similarity change index.

Described self-referencing cavity enhanced spectroscopy (SRCES) systems and methods are tailored to acquiring spectra in a middle regime, in which signals are lower than optimal for conventional absorption spectroscopy, and absorption is higher than optimal for cavity ring-down spectroscopy (CRDS). Longitudinal mode resonance spectral peaks are analyzed individually to extract intensity ratios (e.g. maximum to minimum) and/or curve-fitting parameters, obviating the need to measure or precisely control the input light intensity.

A spectrometer is provided. In one implementation, for example, a spectrometer comprises an excitation source, a focusing lens, a movable mirror, and an actuator assembly. The focusing lens is adapted to focus an incident beam from the excitation source. The actuator assembly is adapted to control the movable mirror to move a focused incident beam across a surface of the sample.

A method of processing raw measurement data from a tunable diode laser absorption spectroscopy (TDLAS) tool or other spectroscopic instrument is provided that determines what types of noise (electronic or process flow) are present in the measurement. Based on that determination, the noise is reduced by performing a weighted averaging using weights selected according to the dominant type of noise present, or a general case is applied to determine weights where neither noise type is dominant. The method also involves performing continuous spectroscopy measurements with the tool, with the data and weighted averaging being constantly updated. Weighting coefficients may also be adjusted based on similarity or difference between time-adjacent traces.

A method of processing raw measurement data from a tunable diode laser absorption spectroscopy (TDLAS) tool or other spectroscopic instrument is provided that determines what types of noise (electronic or process flow) are present in the measurement. Based on that determination, the noise is reduced by performing a weighted averaging using weights selected according to the dominant type of noise present, or a general case is applied to determine weights where neither noise type is dominant. The method also involves performing continuous spectroscopy measurements with the tool, with the data and weighted averaging being constantly updated. Weighting coefficients may also be adjusted based on similarity or difference between time-adjacent traces.

A method of processing raw measurement data from a tunable diode laser absorption spectroscopy (TDLAS) tool or other spectroscopic instrument is provided that determines what types of noise (electronic or process flow) are present in the measurement. Based on that determination, the noise is reduced by performing a weighted averaging using weights selected according to the dominant type of noise present, or a general case is applied to determine weights where neither noise type is dominant. The method also involves performing continuous spectroscopy measurements with the tool, with the data and weighted averaging being constantly updated. Weighting coefficients may also be adjusted based on similarity or difference between time-adjacent traces.

An apparatus and method for monitoring a stability of a spectrum are provided. The apparatus for monitoring stability of a spectrum includes a spectroscope configured to measure a spectrum of a sample and a processor configured to calculate a similarity change index of the measured spectrum and to determine the stability of the measured spectrum by analyzing the calculated similarity change index.

A method of processing raw measurement data from a tunable diode laser absorption spectroscopy (TDLAS) tool or other spectroscopic instrument is provided that determines what types of noise (electronic or process flow) are present in the measurement. Based on that determination, the noise is reduced by performing a weighted averaging using weights selected according to the dominant type of noise present, or a general case is applied to determine weights where neither noise type is dominant. The method also involves performing continuous spectroscopy measurements with the tool, with the data and weighted averaging being constantly updated. Weighting coefficients may also be adjusted based on similarity or difference between time-adjacent traces.

A method is described for estimating a spectral feature of a pulsed light beam produced by an optical source and directed toward a wafer of a lithography apparatus. The method includes receiving a set of N optical spectra of pulses of the light beam; saving the received N optical spectra to a saved set; transforming the optical spectra in the saved set to form a set of transformed optical spectra; averaging the transformed optical spectra to form an averaged spectrum; and estimating a spectral feature of the pulsed light beam based on the averaged spectrum.

Disclosed are a correction system and method for eliminating non-uniform distribution of a light field during hyperspectral image acquisition, to effectively eliminate the impact of non-uniform light field distribution caused by halogen light illumination on acquisition of hyperspectral image information. The correction method includes acquiring hyperspectral images A corresponding to two standard whiteboards with different reflectance under illumination of a halogen light source. A hyperspectral image B is acquired corresponding to a tea leaf sample under illumination of the same light source. Spatial distribution characteristics of a light field are obtained based on the hyperspectral images A. Pixels in the hyperspectral images A and B are spatially matched. Light field correction is performed on a pixel of the sample in the hyperspectral image B, and reflectance correction is performed on the sample after the light field correction.