San Jose, CA, United States of America

Jeffrey Drue David

This inventor holds 107 USPTO granted patents and 20 published patent applications, primarily in Semiconductor Manufacturing (CPC class B24B37-013). Top assignees: Applied Materials, Inc., Pdf Solutions, Incorporated, Stream Mosaic, Inc.. Active years: 2002-2025.

USPTO Granted Patents = 107 

% Patents Active = 86.0

Average Co-Inventor Count = 3.0

ph-index = 14

Forward Citations = 649(Granted Patents)

Forward Citations (Not Self Cited) = 415(Dec 10, 2025)


Inventors with similar research interests:

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Location History:

  • Sunnyvale, CA (US) (2002)
  • Santa Clara, CA (US) (2014)
  • San Jose, CA (US) (2005 - 2024)

Company Filing History:


Years Active: 2002-2025

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Areas of Expertise:
Wafer Bin Map
Root Cause Analysis
Collaborative Learning
Abnormal Image Classification
Endpoint Detection
Chemical Mechanical Polishing
Spectral Data
Yield Prediction
Process Control
Polishing Techniques
Optical Monitoring
Dimensional Reduction
107 patents (USPTO):Explore Patents

Title: A Profile of Inventor Jeffrey Drue David

Introduction

Jeffrey Drue David is a notable inventor based in San Jose, CA. With an impressive portfolio of 107 patents, he has significantly contributed to the field of semiconductor technology. His recent innovations showcase a commitment to advancing manufacturing processes and enhancing product quality.

Latest Patents

One of David's latest patents is the "Wafer Bin Map Based Root Cause Analysis." This patent presents a template for identifying the most probable root causes of wafer defects by comparing the bin map data of a subject wafer with that of prior wafers. By doing so, it allows for a probability assessment of whether the same root cause should apply to the subject wafer, facilitating accurate labeling.

Another significant patent is the "Collaborative Learning Model for Semiconductor Applications." This patent focuses on wafer classification through a collaborative learning approach. It combines initial classifications determined by a rule-based model with predictions from a machine learning model. The method enables multiple users to review, confirm, modify, or add classifications manually, with all input contributing to the continuous improvement of the machine learning detection and classification scheme.

Career Highlights

Throughout his career, David has worked with reputable companies in the semiconductor industry. Notable affiliations include Applied Materials, Inc. and Pdf Solutions, Incorporated. His expertise in semiconductor applications has been pivotal in developing innovative technologies that address industry challenges.

Collaborations

David has collaborated with esteemed colleagues such as Dominic J. Benvegnu and Boguslaw A. Swedek. Their combined efforts have fostered an environment of innovation and success, driving advancements in semiconductor technology.

Conclusion

Jeffrey Drue David's contributions to the field of semiconductor technology are impressive and impactful. With a rich history of patents and a career marked by influential collaborations, he continues to play a vital role in shaping the future of semiconductor applications. His latest innovations underscore his commitment to enhancing efficiency and accuracy in the industry.

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