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EGR 590 633 Data Science for Semiconductor Manufacturing
Spring 2027

EGR 590 633 Data Science for Semiconductor Manufacturing

3 Credit Hours

RR
Rathziel Roncancio Reyes

Faculty

This course introduces the fundamentals of semiconductor manufacturing and the data-science tools used in modern fabrication environments. Students examine semiconductor processes, the data generated throughout manufacturing, and representative applications such as defect detection, predictive maintenance, cycle-time prediction, and yield optimization.

The course is designed for students from diverse backgrounds and does not require prior semiconductor or data-science experience.

Prerequisites

Prior experience with R, Python, or SQL is recommended but not required. Introductory tutorials will be provided. Students should be comfortable working with common computer applications and learning new analytical software.

Course Objectives

Upon completing this course, students will be able to:

  • Describe major semiconductor fabrication processes and their influence on manufacturing outcomes.
  • Identify manufacturing data from equipment, metrology, quality, production, and manufacturing-execution systems.
  • Acquire, query, transform, and analyze manufacturing data with industry-relevant tools.
  • Apply statistical and predictive methods to process variability, yield, cycle time, and operational performance.
  • Evaluate and communicate data-driven solutions involving big-data analytics, Industry 4.0, cyber-physical systems, and digital twins.

Course Outline

The course is organized around three broad areas:

  1. Semiconductor fabrication foundations
    Silicon manufacturing, film formation, epitaxy, photolithography, planarization, etching, doping, process integration, and packaging.
  2. Semiconductor data and analytical tools
    Manufacturing data sources, statistics, distributions, sampling, SQL and data querying, JMP, and JSL.
  3. Data-science applications and Industry 4.0
    Big-data analytics, cyber-physical systems, digital twins, cycle-time and output prediction, defect-pattern recognition, and yield learning.

Course Requirements

The course is delivered asynchronously through Moodle. Learning activities include assigned readings, online discussions, quizzes, applied data-analysis assignments, and an independent final project. The project uses a data source selected by the student and includes a short paper and documented analysis code.

Specific schedules, formats, and grading weights will be provided in the course syllabus.

Textbooks

Primary texts, available online through NC State University Libraries:

  • T.-C. T. Chen, Production Planning and Control in Semiconductor Manufacturing, 2023.
  • Gary S. May and Costas J. Spanos, Fundamentals of Semiconductor Manufacturing and Process Control, 2006.
  • Joe Reis and Matt Housley, Fundamentals of Data Engineering, 2022.

Recommended: Y. Lian, Semiconductor Microchips and Fabrication, 2023. Additional readings may be provided through the course site.

Software Requirements

  • A laptop computer with reliable internet access and a modern web browser
  • MATLAB, using the NC State license or MATLAB Online
  • JMP or JMP Student Edition

Additional web-based resources and installation instructions will be provided through the course site.

Created: 10/06/2026.