Kla Tencor Corporation

Kla Corporation

365 Retail Markets, LLC

Himanshu Vajaria

Bradley Ries

Mohan Mahadevan

Yong Zhang

Jan A Lauber

Tommaso Torelli

Shabnam Ghadar

Sina Jahanbin

Stilian Pandev

Krishna Vedula

Anuj Pandey

Rahul Lakhawat

A merchandiser device includes a storage area including multiple rows, in which each row includes multiple lanes, and in which each lane is configured to store multiple products. The merchandiser device includes a time of flight sensor positioned for use with a lane and configured to produce sensor output indicative of an event associated with a product stored in the lane. An event associated with the product is detected based on the sensor output. Identifying information for the product is determined based on the sensor output and a planogram of the merchandiser device. A quantity of the product associated with the event is determined based on the sensor output and dimensional information associated with the product. Data configured to cause an update to an inventory record associated with the product is then output based on the identifying information and the quantity.

Distance-based product event detection

The present disclosure describes methods, systems, and articles of manufacture for performing a defect inspection of a die image using adaptive care areas (ACAs). The use of ACAs solve the problem of handling rotations of components that require rotating care areas; handling the situation where each care area requires its own rotation, translation, or affine transformation; and the situation of decoupling intensity differences caused by defects or process variation from intensity differences caused by size variations.

Adaptive care areas for die-die inspection

A system for analyzing a sample includes an inspection sub-system and at least one controller. The inspection sub-system is configured to scan a sample to collect a first plurality of sample images having a first image resolution. The controller is configured to generate a defect list based on the first plurality of sample images. The controller is further configured to input images corresponding to the defect list into a neural network that is trained with source data including sample images having the first image resolution and sample images having a second image resolution higher than the first image resolution. The controller is further configured to generate a second plurality of sample images with the neural network based on the images corresponding to the defect list, where the second plurality of sample images have the second image resolution and correspond to the defect list.

Super-resolution defect review image generation through generative adversarial networks

A die-die inspection image can be aligned using a method or system configured to receive a reference image and a test image, determine a global offset and rotation angle from local sections on the reference image and test image, and perform a rough alignment de-skew of the test image prior to performing a fine alignment.

Multi-step image alignment method for large offset die-die inspection

Feature extraction and classification is used for process window monitoring. A classifier, based on combinations of metrics of masked die images and including a set of significant combinations of one or more segment masks, metrics, and wafer images, is capable of detecting a process non-compliance. A process status can be determined using a classifier based on calculated metrics. The classifier may learn from nominal data.

Feature selection and automated process window monitoring through outlier detection

Methods and systems for monitoring process tool conditions are disclosed. The method combines single wafer, multiple wafers within a single lot and multiple lot information together statistically as input to a custom classification engine that can consume single or multiple scan, channel, wafer and lot to determine process tool status.

Wafer and lot based hierarchical method combining customized metrics with a global classification methodology to monitor process tool condition at extremely high throughput

Shadow-grams are used for edge inspection and metrology of a stacked wafer. The system includes a light source that directs collimated light at an edge of the stacked wafer, a detector opposite the light source, and a controller connected to the detector. The stacked wafer can rotate with respect to the light source. The controller analyzes a shadow-gram image of the edge of the stacked wafer. Measurements of a silhouette of the stacked wafer in the shadow-gram image are compared to predetermined measurements. Multiple shadow-gram images at different points along the edge of the stacked wafer can be aggregated and analyzed.

Automated inline inspection and metrology using shadow-gram images

Methods and devices are disclosed for automated detection of a status of wafer fabrication process based on images. The methods advantageously use segment masks to enhance the signal-to-noise ratio of the images. Metrics are then calculated for the segment mask variations in order to determine one or more combinations of segment masks and metrics that are predictive of a process non-compliance. A model can be generated as a result of the process. In another embodiment, a method uses a model to monitor a process for compliance.

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San Jose, CA, United States of America

Himanshu Vajaria