A novel driving system for PDPs called the “PLASMA AI” has been developed achieving more than a 50% increase in peak brightness of the image reproduced while maintaining the power consumption almost unchanged. The image quality is so improved that the PDP monitor is now applicable for the use in large‐screen home TV.

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Extreme ultraviolet lithography has advanced microfabrication of semiconductor devices toward the sub-10-nm generation. In this situation, stochastic defects increase and hence process evaluation requires an entire wafer inspection at high speed. To satisfy this requirement, a large field of view (FoV) inspection with low-resolution enables us to inspect an entire wafer within an acceptable time because the throughput of e-beam inspection depends on imaging resolution. However, low-resolution images are difficult to inspect at high precision using conventional methods because of a smaller photographed defect size and worse signal-to-noise ratio. Moreover, deformation caused by the manufacturing process and larger distortion caused by large FoV result in false detections when we apply die-to-database (D2DB) inspection. To solve these issues, we propose trainable D2DB inspection, which predicts a pixel-value distribution of normal images from a corresponding design layout. The proposed method is robust to lowresolution images because it considers noise and acceptable deformation as variance of the learned distribution. In addition, by introducing a model to predict a misalignment between a design layout and inspection image, trainable D2DB becomes robust to image distortion. Experiments show that trainable D2DB can perform high-precision inspection on images with large noise and image distortion.

Visual explanations are important to increase models' transparency. Grad-CAM [1] is an effective method because of its high class discrimination, no requirement of architectural changes, and so on. However, in detection tasks, because Grad-CAM only focuses on the importance of features but does not have spatial sensitivity, it generates heatmaps in which not related regions to detected objects are also highlighted. In this study, we propose Spatial Sensitive Grad-CAM (SSGrad-CAM), which can generate appropriate heatmaps for object detectors. SSGrad-CAM modifies the heatmap generated from Grad-CAM with space maps computed by normalizing the magnitude of gradients. In this manner, SSGrad-CAM can incorporate spatial sensitivity and focus on the importance of both features and space. Through experiments, we confirm SSGrad-CAM can generate appropriate heatmaps for detection results, and also confirm it can generate when models detect objects by paying high attention to their peripheral regions, as well.

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