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Abstract The demand for high-resolution and large-area imaging systems for non-destructive wafer inspection has grown owing to the increasing complexity and extremely fine nature of semiconductor processes. Several studies have focused on developing high-resolution imaging systems; however, they were limited by the tradeoff between image resolution and field of view. Hence, computational imaging has arisen as an alternative method to conventional optical imaging, aimed at enhancing the aforementioned parameters. This study proposes a method for improving the resolution and field of view of an image in a lens-less reflection-type system. Our method was verified by computationally restoring the final image from diffraction images measured at various illumination positions using a visible light source. We introduced speckle illumination to expand the numerical aperture of the entire system, simultaneously improving image resolution and field of view. The image reconstruction process was accelerated by employing a convolutional neural network. Using the reconstructed phase images, we implemented high-resolution topography and demonstrated its applicability in wafer surface inspection. Furthermore, we demonstrated an ideal diffraction-limited spatial resolution of 1.7 μm over a field of view of 1.8 × 1.8 mm2 for the topographic imaging of targets with various surface roughness. The proposed approach is suitable for applications that simultaneously require high throughput and resolution, such as wafer-wide integrated metrology, owing to its compact design, cost-effectiveness, and mechanical robustness.

Abstract Colorimetric sensing, which provides effective detection of bio-molecular signals with one’s naked eye, is an exceptionally promising sensing technique in that it enables convenient detection and simplification of entire sensing system. Though colorimetric sensors based on all-dielectric nanostructures have potential to exhibit distinct color variations enabling manageable detection due to their trivial intrinsic loss, there is crucial limitation that the sensitivity to environmental changes lags behind their plasmonic counterparts because of relatively small region of near field-analyte interaction of the dielectric Mie-type resonator. To overcome this challenge, we proposed all-dielectric metasurface colorimetric sensor which exhibits dual-resonance in the visible region. Thereafter, we confirmed with simulation that, in the elaborately designed dual-Lorentzian-type spectra, highly perceptible variations of structural color were manifested even in minute change of peripheral refractive index. In addition to verifying physical effectiveness of the superior colorimetric sensing performance appearing in the dual-resonance type sensor, by combining advanced optimization technique utilizing deep neural networks, we attempted to maximize sensing performance while obtaining dramatic improvement of design efficiency. Through well-trained deep neural network that accurately simulates the input target spectrum, we numerically verified that designed colorimetric sensor shows a remarkable sensing resolution distinguishable up to change of refractive index of 0.0086.