SEMU-Net: A Segmentation-based Corrector for Fabrication Process Variations of Nanophotonics with Microscopic Images

TL;DR

SEMU-Net is introduced, a comprehensive set of methods that automatically segments scanning electron microscope images and uses them to train two deep neural network models based on U-Net and its variants, ensuring that the final fabricated structures closely align with the intended specifications.

Abstract

Integrated silicon photonic devices, which manipulate light to transmit and process information on a silicon-on-insulator chip, are highly sensitive to structural variations. Minor deviations during nanofabrication-the precise process of building structures at the nanometer scale-such as over- or under-etching, corner rounding, and unintended defects, can significantly impact performance. To address these challenges, we introduce SEMU-Net, a comprehensive set of methods that automatically segments scanning electron microscope images (SEM) and uses them to train two deep neural network models based on U-Net and its variants. The predictor model anticipates fabrication-induced variations, while the corrector model adjusts the design to address these issues, ensuring that the final fabricated structures closely align with the intended specifications. Experimental results show that the segmentation U-Net reaches an average IoU score of 99.30%, while the corrector attention U-Net in a tandem architecture achieves an average IoU score of 98.67%.

Figures (6)

• Figure 1: Overview of the SEMU-Net framework for improving the fabrication of integrated silicon photonic devices. (i) The segmentation model converts SEM images into segmented SEM images. (ii) The corrector model uses the binarized SEM images along with their corresponding GDS design files to train itself, generating corrected GDS layouts that compensate for fabrication-induced variations. (iii) The predictor model uses the corrected GDS files to predict the final fabricated structures, enabling pre-fabrication validation and further refinement.
• Figure 2: A sample from the ANT NanoSOI dataset: (a) an SEM image of a photonics structure with silicon (gray) and silica (black), (b) its segmented SEM image, (c) its corresponding GDS design file, and (d) the difference between the segmented SEM image and the GDS design file.
• Figure 3: Overview of the segmentation, predictor, and corrector models, all based on the U-Net architecture. The corrector model distinguishes itself by incorporating attention gates (labeled AG) in the decoder path, whereas the segmentation and predictor models do not utilize attention gates. The segmentation model takes SEM images as input and generates segmented labels, while the corrector model takes GDS designs as input and outputs corrected designs.
• Figure 4: Performance comparison between the attention U-Net in tandem configuration and the original U-Net across three sample images. Each sample presents (i) the original design and its predicted structure post-fabrication, highlighting corner rounding and structural deviations that could impact device performance, (ii) the correction and prediction of correction for the tandem attention U-Net, and (iii) the correction and prediction of correction for the original U-Net. The tandem attention U-Net outperforms the original U-Net, demonstrating better structural fidelity and achieving a higher IoU score.
• Figure 5: Workflow of the threshold-based segmentation model. A combination of Canny and Otsu's methods is used to detect the contours and further segment the pre-processed SEM images.