Kla Tencor Corporation

Kla Corporation

Rahul Lakhawat

Lisheng Gao

Martin Plihal

Kenong Wu

Hong Chen

Santosh Bhattacharyya

Yong Zhang

Raghav Babulnath

Himanshu Vajaria

Bradley Ries

Thirupurasundari Jayaraman

Raghavan Konuru

Anuj Pandey

Srikanth Kandukuri

Pavan Kumar

Ravikumar Sanapala

Naoshin Haque

Oksen Toros Baris

Gangadharan Sivaraman

Karthikeyan Subramanian

Vidur Pandita

Vivek Bhagat

Rajesh Ramachandran

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

Methods and systems for determining a position of output generated by an inspection subsystem in design data space are provided. In general, some embodiments described herein are configured for substantially accurately aligning inspection subsystem output generated for a specimen to a design for the specimen despite deformation of the design in the inspection subsystem output. In addition, some embodiments are configured for generating and/or using alignment targets that can be shared across multiple specimens of the same layer and design rule for alignment of inspection subsystem output generated for a specimen to a design for the specimen.

Sub-pixel alignment of inspection to design

Systems and methods for discovering defects on a wafer are provided. One method includes detecting defects on a wafer by applying a threshold to output generated by a detector in a first scan of the wafer and determining values for features of the detected defects. The method also includes automatically ranking the features, identifying feature cut-lines to group the defect into bins, and, for each of the bins, determining one or more parameters that if applied to the values for the features of the defects in each of the bins will result in a predetermined number of the defects in each of the bins. The method also includes applying the one or more determined parameters to the output generated by the detector in a second scan of the wafer to generate a defect population that has a predetermined defect count and is diversified in the values for the features.

Wafer defect discovery

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Chennai, India

Rahul Lakhawat