Applied Materials, Inc.

The Dun Bradstreet Corporation

Alexander T Schwarm

Arulkumar P Shanmugasundram

Yuri Kokotov

Jacques Seror

Efim Entin

Rinat Shimshi

Vicky Svidenko

Youval Nehmadi

Yossi Fisher

Gopalakrishna B Prabhu

Y Sean Lin

Jerry Lynn Harvey, Jr

James Beveridge

Sundar Jawaharlal

Moshe Sarfaty

Young Jeen Paik

Jehuda Hartman

Shalomo Sarel

Anudit Vikram

Rong Pan

Suketu Arun Parikh

Martin Jay Seamons

Helen R Armer

Ilias Iliopoulos

Charles Gay

Manuel Hernandez

Terry P Reiss

Alexander Parkhomovsky

Sanjiv Mittal

William R Clements, Iii

Sundar Jawaharlah

Amna Mohammad

Granger Huntress

Lavina Choudhary

Bradley White

Dane Anthony Macaulay

Karolina Kierzkowski

Nalanda Matia

Nicholas Lizotte

A method and system for identifying and classifying Visitor Information tracked on websites to identify Internet Service Providers (ISPs) and non-Internet Service Providers (non-ISPs). The technology employs machine intelligence to train a classifier on firmographically-enriched Visitor Intelligence from website tracking technology. The ISP classifier can distinguish ISPs from non-ISPs to identify website traffic for a given website that is attributable to ISPs.

Machine learning classifier for identifying internet service providers from website tracking

Embodiments of a system and method for identifying and prioritizing company prospects by training at least one classifier on client company win/loss metrics. One or more classifiers can be trained on a training database compiled from company win/loss database for a client and firmographic data from a robust business entity database. Once trained, the system can employ Artificial Intelligence powered by the trained classifiers to classify and output customized prospect lists of thousands of profiled and scored companies that the AI has determined are likely targets for specific marketing and sales. The AI can also ingest databases of client targets and classify and score them based on the custom-trained classifier.

Machine learning classifier and prediction engine for artificial intelligence optimized prospect determination on win/loss classification

A method and system for employing a Language Processing machine learning Artificial Intelligence engine to employ word embeddings and term frequency-inverse document frequency to create numerical representations of document meaning in a high-dimensional semantic space or an overall semantic direction. This semantic direction can be used to quantitatively measure semantic similarity between online content consumed by a potential prospect and a given product or product family. The AI can automate the process of creating audiences for on-line marketplaces for programmatic advertising purposes by using representative product descriptions, such as a grouping of product descriptions for scalable, cloud-based databases, and then creating a hyper-focused intent-based audience based on companies that are showing a significant increase in intent.

Artificial intelligence engine for generating semantic directions for websites for automated entity targeting to mapped identities

Embodiments of the present invention provide methods and apparatuses for determining factors for design consideration in yield analysis of semiconductor fabrication. In one embodiment, a computer-implemented method for determining factors for design consideration in yield analysis of semiconductor fabrication includes obtaining a geometric characteristic of a defect on a chip and obtaining design data of the chip, where the design data is associated with the defect. The method further includes determining a criticality factor of the defect based on the geometric characteristic and the design data, and outputting the criticality factor.

Method and apparatus for determining factors for design consideration in yield analysis

A factory control server stores module configuration data for modules. The modules include processes for producing a final product and have corresponding module requirements. The factory control server analyzes in real-time actual product output data that is generated by a final product tester after a factory produces at least one final product to determine whether the actual product output data meets an expected product output. The factory control server analyzes actual module data in real-time to determine a new module requirement to cause new actual product output data for a subsequent final product to meet the expected product output in response to a determination that the actual product output data does not meet the expected product output. The factory control server notifies a module controller in real-time of the new module requirement. The module controller changes parameters in real-time to manufacture the subsequent final product.

Factory level process and final product performance control system

In one embodiment, an inline defect analysis method includes receiving geometric characteristics of individual defects and design data corresponding to the individual defects, determining which of the individual defects are likely to be nuisance defects using the geometric characteristics and the corresponding design data, and refraining from sampling the defects that are likely to be nuisance defects.

Inline defect analysis for sampling and SPC

A method of controlling surface non-uniformity of a wafer in a polishing operation includes (a) providing a model for a wafer polishing that defines a plurality of regions on a wafer and identifies a wafer material removal rate in a polishing step of a polishing process for each of the regions, wherein the polishing process comprises a plurality of polishing steps, (b) polishing a wafer using a first polishing recipe based upon an incoming wafer thickness profile, (c) determining a wafer thickness profile for the post-polished wafer of step (b), and (d) calculating an updated polishing recipe based upon the wafer thickness profile of step (c) and the model of step (a) to maintain a target wafer thickness profile. The model can information about the tool state to improve the model quality. The method can be used to provide feedback to a plurality of platen stations.

Feedback control of a chemical mechanical polishing device providing manipulation of removal rate profiles

A scheduler system obtains a basic model of a manufacturing process for the production of one or more products. The basic model is based on a first set of data collected at a point in time from a plurality of tools used to manufacture the one or more products. The system incorporates a second set of data, which is collected from the plurality of tools after the first set of data, into the basic model to generate a comprehensive model of the manufacturing process. The second set of data reflects a current state of a factory. The system evaluates a plurality of scheduling policies using the comprehensive model and selects an optimal scheduling policy from the plurality of scheduling policies based on the comprehensive process model evaluation to achieve a manufacturing objective.

Scheduling modeling system for adaptive, automated data collection and performance analysis of manufacturing system for optimal scheduling

Feedback control of chemical mechanical polishing device providing manipulation of removal rate profiles

A method and apparatus for diagnosing faults. Process data is analyzed using a first metric to identify a fault. The process data was obtained from a manufacturing machine running a first recipe. A fault signature that matches the fault is identified. The identified fault signature was generated using a second metric and/or a second recipe. At least one fault class that is associated with the fault signature is identified.

Metrics independent and recipe independent fault classes

A method, system, and medium of modeling and/or for controlling a manufacturing process is disclosed. The method includes the steps of identifying one or more input parameters that cause a change in output characteristics, defining global nodes using estimated maximum and minimum values of the input parameters, and defining a mathematical equation that calculates a predicted output characteristic for each node. The method also includes the steps of receiving at least one empirical data point having one or more input parameter values and at least one empirical output value and adjusting the predicted output values at the nodes based on a difference between the at least one empirical output value and the predicted output characteristic calculated using the mathematical equation based on the one or more input parameter values.

Method, system and medium for controlling manufacturing process using adaptive models based on empirical data

A method, system, and medium of modeling and/or for controlling a manufacturing process is disclosed. In particular, a method according to embodiments of the present invention includes calculating a set of predicted output values, and obtaining a prediction model based on a set of input parameters, the set of predicted output values, and empirical output values. Each input parameter causes a change in at least two outputs. The method also includes optimizing the prediction model by minimizing differences between the set of predicted output values and the empirical output values, and adjusting the set of input parameters to obtain a set of desired output values to control the manufacturing apparatus. Obtaining the prediction model includes transforming the set of input parameters into transformed input values using a transformation function of multiple coefficient values, and calculating the predicted output values using the transformed input values.

Method, system and medium for controlling manufacture process having multivariate input parameters

In one embodiment, a method for predicting yield during the design stage includes receiving defectivity data identifying defects associated with previous wafer designs, and dividing the defects into systematic defects and random defects. For each design layout of a new wafer design, yield is predicted separately for the systematic defects and the random defects. A combined yield is then calculated based on the yield predicted for the systematic defects and the yield predicted for the random defects.

Stage yield prediction

In one embodiment, a method for predicting yield includes calculating a criticality factor (CF) for each of a plurality of defects detected in an inspection process step of a wafer, and determining a yield-loss contribution of the inspection process step to the final yield based on CFs of the plurality of defects and the yield model built for a relevant design. The yield-loss contribution of the inspection process step is then used to predict the final yield for the wafer.

Dynamic inline yield analysis and prediction of a defect limited yield using inline inspection defects

Process data is analyzed, the process data having been generated during a manufacturing process to detect a fault. One or more process variables of the manufacturing process that contributed to the fault are determined. A relative contribution of each of the one or more process variables to the fault is determined. A fault signature having relative contribution ranges for at least one of the one or more process variables is generated, the relative contribution ranges based on the determined relative contributions.

Ranged fault signatures for fault diagnosis

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Austin, TX, United States of America

Alexander T Schwarm