Total Reflection X-ray Fluorescence (TXRF) analysis is a non-destructive and surface-sensitive analysis method using X-rays, in which incident X-rays are irradiated on a sample at an extremely low grazing angle (about 0.1°) and the fluorescent X-rays from the sample generated by the incident X-rays are measured with extremely low background because of the total reflection characteristics of the incident X-rays. TXRF analysis does not require special sample preparation for flat samples. Because of this, TXRF analysis has been widely used for the evaluation of contamination on wafers in semiconductor manufacturing processes(3) as well as in industrial and environmental analysis. Contamination control in semiconductor manufacturing processes becomes more rigorous every year.
Recently, Artificial Intelligence (AI) technologies have developed rapidly along with progress in computer hardware, software and software libraries to deal with big data. One main benefit of AI is that it automatically extracts and analyzes unique and notable characteristics from a huge amount of data. In the field of image processing, particularly, image recognition—for example, handwritten character recognition—has been actively researched and many results—such as super-resolution techniques that convert low-resolution images to high-resolution ones—have been achieved. Although there are many cases where AI is used for image processing, it seems that there are few cases where AI technologies are applied to one-dimensional spectrum analysis instead of to a two-dimensional image. Therefore, in this paper, we applied the machine learning method to the data processing of TXRF analysis and introduce the results, especially on the quantification of contaminations on wafers from the spectrum obtained by short-time measurements.