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To enable high-density optical storage, better storage media structures, diversified recording methods, and improved accuracy of readout schemes should be considered. In this study, we propose a novel three-dimensional (3D) sloppy nanostructure as the optical storage device, and this nanostructure can be fabricated using the 3D laser direct writing technology. It is a 900 nm high, 1 × 2 µm wide Si slope on a 200 nm SiO2 layer with 200 nm Si3N4 deposited on top to enhance reflectivity. In this study, we propose a reflected spectrum-based method as the readout recording strategy to stabilize information readout more stable. The corresponding reflected spectrum varied when the side wall angle of the slope and the azimuth angle of the nanostructure were tuned. In addition, an artificial neural network was applied to readout the stored information from the reflected spectrum. To simulate the realistic fabrication error and measurement error, a 20% noise level was added to the study. Our findings showed that the readout accuracy was 99.86% for all 120 data sequences when the slope and azimuth angle were varied. We investigated the possibility of a higher storage density to fully demonstrate the storage superiority of this designed structure. Our findings also showed that the readout accuracy can reach its highest level at 97.25% when the storage step of the encoded structure becomes 7.5 times smaller. The study provides the possibility to further explore different nanostructures to achieve high-density optical storage.

In integrated circuit manufacturing, optical critical dimension measurement is an efficient and non-destructive metrology method. It is also a model-based metrology in which a numerical model of the target device is formed to simulate the optical spectrum. The result is then reconstructed by fitting the simulated spectrum to the experimentally measured optical spectrum. Normally, the measured optical spectrum contains a great deal of data points that consume the storage space, and increase the fitting time. Therefore, it is worth finding an appropriate approach to downsample these data points without losing much accuracy. To quickly and accurately extract critical data with high sensitivity, we propose a Laplace sensitivity operator that is widely used for feature extraction. Compared with traditional sensitivity calculation, the Laplace sensitivity operator focuses more on the correlation and coupling between multiple parameters. Thus, the sensitivity can be properly analyzed from different dimensions. To test the feasibility and correctness of the proposed method, three basic structures were used for single-parameter verification: thin film, one-dimensional grating, and two-dimensional grating, and a vertical gate-all-around device used for multi-parameter analysis. Using the Laplace sensitivity operator, the extracted data showed better results in most cases than those achieved by the traditional sensitivity calculation method. The data volume was compressed by approximately 70%, the result matching loss was not significantly increase in terms of the root mean square error, and the calculation speed was increased by a factor of 2.4. Compared to the traditional sensitivity operator, the Laplace sensitivity operator was able to reduce the RMSE by up to 50%.

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