To increase the throughput of image-based wafer quality inspection tools, we propose to use computational imaging methods to address two main bottlenecks: the mechanical alignment of the wafer with the imaging plane and pixel size of the imager. The former requires significant time but is crucial for a reliable quality check, and the latter introduces a trade off between the wafer scanning speed and the minimum detectable defect size. We demonstrate application of our recently developed SANDR algorithm for obtaining a wafer image with sub-pixel resolution from a series of not-perfectly aligned low-resolution images. The wafer misalignment creates a varying over the field of view depth-of-focus, which poses a significant obstacle for the state-of-the-art methods, but is successfully processed with SANDR. The method is tested on simulated images.
We demonstrate a novel closed-loop input design technique on the detection of particles in an imaging system such as a fluorescence microscope. The probability of misdiagnosis is minimized while constraining the input energy such that for instance phototoxicity is reduced. The key novelty of the closed-loop design is that each next input is designed based on the most recent information. Using updated hypothesis probabilities, the input energy distribution is optimized for detection such that unresolved pixels have increased illumination next image acquisition. As compared to conventional open-loop, the results show that (regions of) particles are diagnosed using less energy in the closed-loop approach. Besides the closed-loop approach being viable for the initialization of fluorescence microscopy measurements, it is the next step to sequential object segmentation for reliable and efficient product inspection in Industry 4.0.
We investigate the general adjustment of projection-based phase retrieval algorithms for use with saturated data. In the phase retrieval problem, model fidelity of experimental data containing a non-zero background level, fixed pattern noise, or overexposure, often presents a serious obstacle for standard algorithms. Recently, it was shown that overexposure can help to increase the signal-to-noise ratio in AI applications. We present our first results in exploring this direction in the phase retrieval problem, using as an example the Gerchberg-Saxton algorithm with simulated data. The proposed method can find application in microscopy, characterisation of precise optical instruments, and machine vision applications of Industry4.0.
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