For imaging methods based on Fourier transform, such as coherent diffraction imaging (CDI), in the case where the sampling rate of the diffraction data satisfies Nyquist sampling theorem, phase retrieval algorithm can effectively recover the phase lost in the image acquisition process. In general, ptychography is used to ensure the adequate sample. But in the imaging system of EUV mask defect inspection, the acquisition of more sampling data requires multiple exposures of the mask, which increases the sampling time resulting in low throughput and causes mask irradiation damage. To solve the problem, we propose a machine learning scheme to reconstruct the profile of phase defect using a single diffraction intensity. Combining the physical model of CDI and neural network framework, the method transforms the phase retrieval problem into a regression model of machine learning. Integrating the constraints of the traditional CDI and the gradient descent algorithms in neural network framework, our approach can accelerate the convergence of the model. The performance of the method is confirmed by comparing with the conventional iterative method in the condition of sufficient sampling rate. This novel method provides a new idea of combining computational imaging with machine learning, which can simplify the data acquisition process and accelerate the iteration of algorithm in EUV mask defect inspection.
The contamination control of silicon wafer surface is more and more strict. Many investigations have been done to inspect defects on silicon wafer. However, rare studies have been reported on defect component inspection, which is also critical to trace the source of defects and monitor manufacturing processes in time. In order to inspect the components of contaminated particles on silicon wafer, especially with a high-speed, in-line mode and negligible damage, a dual nanosecond pulse laser system with both wavelengths at 532 nm is designed, in which one laser pumps the particles away from the wafer surface with negligible damage, the other laser breaks down the particles in the air above the wafer surface to obtain the emission lines of the contaminated particles by a spectroscopy with intensified charge coupled device. The sensitivity of the dual pulse laser system is evaluated. The particle dynamic process after pump is analyzed. The results in this work provide a potential on-line method for the semiconductor industry to trace the sources of defects during the manufacture process.
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