Kla-tencor Technologies Corporation

Applied Materials, Inc.

Kla Tencor Corporation

Prashant Aji

Shifang Li

Todd J Egan

Lena Nicolaides

Lei Lian

Leonard Michael Tedeschi

Youxian Wen

Priyadarshi Panda

Pengyu Han

Sharon McCauley

Eli Mor

David Winslow Randall

Alex J Tom

David Bruce Coldren

Mehdi Vaez-Iravani

Guoheng Zhao

Mehran Nasser-Ghodsi

Stanley E Stokowski

Rudy Flores Garcia

Gregory Kirk

Isabella Talley Lewis

Robert M Danen

Steven R Lange

Mohan Mahadevan

Paul Horn

Wolfgang Vollrath

Xinyuan Chong

Mark Borowicz

David R Bakker

Ben-Ming Benjamin Tsai

Dave Bakker

Tzu Chin Chuang

Sven Schwitalla

Chengqing Wang

Ian McDonald

Michael Gasvoda

Stephane Durant

James L Belliveau

Chacko C Neroth

A cool cluster comprises one or more transfer chambers; a plurality of process chambers connected to the one or more transfer chambers; and a computing device of the tool cluster. The computing device is to receive first measurements generated by sensors of a first process chamber during or after a process is performed within the first process chamber; determine that the first process chamber is due for maintenance based on processing the first measurements using a first trained machine learning model; after maintenance has been performed on the first process chamber, receive second measurements generated by the sensors during or after a seasoning process is performed within the first process chamber; and determine that the first process chamber is ready to be brought back into service based on processing the second measurements using a second trained machine learning model.

Autonomous substrate processing system

Systems, methods, and computer-readable mediums for monitoring temperature of a substrate are described. Spectroscopic measurements are performed on a surface of the substrate using a metrology tool integrated with a processing tool. The measurements may be used to determine that the substrate has cooled below a threshold temperature using the spectroscopic measurements.

Using spectroscopic measurements for substrate temperature monitoring

A substrate processing system comprises one or more transfer chambers; a plurality of process chambers connected to the one or more transfer chambers; and a computing device connected to each of the plurality of process chambers. The computing device is to receive first measurements generated by sensors of a first process chamber during or after a process is performed within the first process chamber; determine that the first process chamber is due for maintenance based on processing the first measurements using a first trained machine learning model; after maintenance has been performed on the first process chamber, receive second measurements generated by the sensors during or after a seasoning process is performed within the first process chamber; and determine that the first process chamber is ready to be brought back into service based on processing the second measurements using a second trained machine learning model.

Systems and methods for enhancing inspection sensitivity to detect defects in wafers using an inspection tool are disclosed. A plurality of light emitting diodes illuminate at least a portion of a wafer and capture a set of grayscale images. A residual signal is determined in each image of the grayscale image set and the residual signal is subtracted from each image of the grayscale image set. Defects are identified based on the subtracted grayscale image set. Models of the inspection tool and wafer may be built and refined in some embodiments of the disclosed systems and methods.

Systems and methods for enhancing inspection sensitivity of an inspection tool

Disclosed are methods and apparatus for inspecting and processing semiconductor wafers. The system includes an edge detection system for receiving each wafer that is to undergo a photolithography process. The edge detection system comprises an illumination channel for directing one or more illumination beams towards a side, top, and bottom edge portion that are within a border region of the wafer. The edge detection system also includes a collection module for collecting and sensing output radiation that is scattered or reflected from the edge portion of the wafer and an analyzer module for locating defects in the edge portion and determining whether each wafer is within specification based on the sensed output radiation for such wafer. The photolithography system is configured for receiving from the edge detection system each wafer that has been found to be within specification. The edge detection system is coupled in-line with the photolithography system.

In-line wafer edge inspection, wafer pre-alignment, and wafer cleaning

Methods and systems for measuring a characteristic of a substrate or preparing a substrate for analysis are provided. One method for measuring a characteristic of a substrate includes removing a portion of a feature on the substrate using an electron beam to expose a cross-sectional profile of a remaining portion of the feature. The feature may be a photoresist feature. The method also includes measuring a characteristic of the cross-sectional profile. A method for preparing a substrate for analysis includes removing a portion of a material on the substrate proximate to a defect using chemical etching in combination with an electron beam. The defect may be a subsurface defect or a partially subsurface defect. Another method for preparing a substrate for analysis includes removing a portion of a material on a substrate proximate to a defect using chemical etching in combination with an electron beam and a light beam.

Methods and systems for measuring a characteristic of a substrate or preparing a substrate for analysis

A method for wafer defect inspection may include, but is not limited to: providing an inspection target; applying at least one defect inspection enhancement to the inspection target; illuminating the inspection target including the at least one inspection enhancement to generate one or more inspection signals associated with one or more features of the inspection target; detecting the inspection signals; and generating one or more inspection parameters from the inspection signals. An inspection target may include, but is not limited to: at least one inspection layer; and at least one inspection enhancement layer.

Optical defect amplification for improved sensitivity on patterned layers

Disclosed are methods and apparatus for automatically organizing and/or analyzing a plurality of defect images without first providing a predefined set of classified images (herein referred to as a training set). In other words, sorting is not based on a training set or predefined classification codes for such defect images. In one embodiment, the defect images each include associated identifying data, such as a fabrication identifier, lot number, wafer number, and layer identifier. Initially, the defect images are sorted according to at least a portion of the associated identifying data into a plurality of 'identifying data groups' or image families. The defect data in each identifying data group is then automatically sorted according to defect appearance. That is, similar defect images are associated with a single bin and similar bins are associated with other similar bins. For example, similar bins are arranged next to each other within a graphical user interface (GUI). A representative feature vector (herein referred to as a 'centroid') is then associated with each bin. The centroid generally represents the images within the particular bin. A search for images that &#x201c;look like&#x201d; a specified target image may then be efficiently performed on a particular identifying data group using the centroids of each bin. The target image's feature vector is compared with the centroid of each bin that is within the same identifying data group or image family as the target image. The techniques of the present invention may also be applied to wafer maps, as well as defect images.

Apparatus and methods for searching through and analyzing defect images and wafer maps

Techniques for efficiently setting up inspection, metrology, and review systems for operating upon semiconductor wafers are described. Specifically, this involves setting up recipes that allows each system to accurately inspect semiconductor wafers. The invention gathers pertinent information from these tools and presents the information to users in a way that greatly reduces the time required to complete a recipe. One system embodiment includes an inspection system and a review station that is communicatively linked such that the review station can read from and write to an entire set of data stored at the inspection system. The set of data includes image files of features detected by the inspection system.

Inspection system setup techniques

Growing community of inventors

San Jose, CA, United States of America

Prashant Aji