Kla Tencor Corporation

Lab2fab LLC

Krishna Rao

Wei Chang

Joseph Gutierrez

Shawn Michael Lange

Ramon Olavarria

Jaydeep K Sinha

Naomi Kalia Williams Zabel

Amir Azordegan

Prasanna Dighe

Hogan Hall Hempy

Michael Christopher DeRenzi

Chad Steese

Robert Freeman Thompson

Malonda Maxine McElwee

Refugio Gonzalez Ponce

Ken Haven

Renata Jaye Puakea Smith

Pradeep Vukkadala

Craig MacNaughton

Derek Pietz

Craig Macnaughton

An automated liquid dispensing attachment monitors and controls preparation of drinks poured from bottles. The automated liquid dispensing attachment attaches to a bottle and senses multiple aspects of the pouring of the drink, and based on the sensor outputs, monitors and controls the flow of fluids from the bottle through the attachment. The automated liquid dispensing attachment communicates with other electronic devices to enable individualized control and monitoring, and data aggregation and analysis.

Connected and automated liquid dispensing attachment

A beverage operations control system of an establishment such as a bar communicates with numerous wireless beverage control devices that monitor and control the pouring of beverages. The system provides monitoring and predictive analysis based on the monitoring, including real-time modifications to the operations of the beverage control devices.

Beverage dispensing and monitoring system

A high-dimensional variable selection unit determines a list of critical parameters from sensor data and parametric tool measurements from a semiconductor manufacturing tool, such as a semiconductor inspection tool or other types of semiconductor manufacturing tools. The high-dimensional variable selection model can be, for example, elastic net, forward-stagewise regression, or least angle regression. The list of critical parameters may be used to design a next generation semiconductor manufacturing tool, to bring the semiconductor manufacturing tool back to a normal status, to match a semiconductor manufacturing tool's results with that of another semiconductor manufacturing tool, or to develop a specification for the semiconductor manufacturing tool.

Determining critical parameters using a high-dimensional variable selection model

Systems and methods for defection classification in a semiconductor process are provided. The system includes a communication line configured to receive a defect image of a wafer from the semiconductor process and a deep-architecture neural network in electronic communication with the communication line. The neural network has a first convolution layer of neurons configured to convolve pixels from the defect image with a filter to generate a first feature map. The neural network also includes a first subsampling layer configured to reduce the size and variation of the first feature map. A classifier is provided for determining a defect classification based on the feature map. The system may include more than one convolution layers and/or subsampling layers. A method includes extracting one or more features from a defect image using a deep-architecture neural network, for example a convolutional neural network.

Automatic defect classification without sampling and feature selection

A method to collect data and train, validate and deploy statistical models to predict overlay errors using patterned wafer geometry data and other relevant information includes selecting a training wafer set, measuring at multiple lithography steps and calculating geometry differences, applying a plurality of predictive models to the training wafer geometry differences and comparing predicted overlay to the measured overlay on the training wafer set. The most accurate predictive model is identified and the results fed-forward to the lithography scanner tool which can correct for these effects and reduce overlay errors during the wafer scan-and-expose processes.

Statistical overlay error prediction for feed forward and feedback correction of overlay errors, root cause analysis and process control

Predictive modeling based focus error prediction method and system are disclosed. The method includes obtaining wafer geometry measurements of a plurality of training wafers and grouping the plurality of training wafers to provide at least one training group based on relative homogeneity of wafer geometry measurements among the plurality of training wafers. For each particular training group of the at least one training group, a predictive model is develop utilizing non-linear predictive modeling. The predictive model establishes correlations between wafer geometry parameters and focus error measurements obtained for each wafer within that particular training group, and the predictive model can be utilized to provide focus error prediction for an incoming wafer belonging to that particular training group.

Predictive wafer modeling based focus error prediction using correlations of wafers

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San Ramon, CA, United States of America

Krishna Rao