用显微图像自动修正光子芯片制造误差，提升精度。
SEMU-Net: A Segmentation-based Corrector for Fabrication Process Variations of Nanophotonics with Microscopic Images
- 通过分割扫描电镜图像，训练U-Net模型识别制造偏差。
- 修正模型在串联架构下实现98.67%的平均IoU，接近设计目标。
- 适合芯片制造、纳米光子学领域研究人员参考。
集成硅光子器件在硅基绝缘芯片上操控光以传输和处理信息，对结构变化极为敏感。纳米制造过程中微小偏差，如过刻/欠刻、角部圆化及意外缺陷，会显著影响性能。为此，我们提出SEMU-Net，一套基于扫描电子显微镜图像（SEM）自动分割并训练两个基于U-Net及其变体的深度神经网络模型的方法。预测模型可提前预判制造引起的结构变化，校正模型则调整设计以缓解这些问题，确保最终制造结构与预期规格高度一致。实验结果表明，分割U-Net平均IoU达99.30%，而串联架构下的校正注意力U-Net平均IoU为98.67%。
原文摘要 · Abstract (English)
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%.
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