Project Description:

Objective of the Project:
This project focuses on applying photolithography fundamentals, data science, statistics, and machine learning to enable breakthroughs in advanced semiconductor process development. The intern will work on analytical projects involving large‑scale data correlation, recipe accuracy enhancement, and multi‑level characterization studies. The work emphasizes modeling, simulation, and predictive analytics without requiring direct wafer processing.

Opportunities to be Offered for Full-Time Employment:
Outstanding performance during the internship may lead to consideration for future full‑time opportunities within Technology Development or related engineering groups.

Project Scope:

Learning Opportunities:

• Gain deep exposure to photolithography concepts including immersion optics, polarized illumination, low‑k₁ imaging, and material behavior.
• Learn how lithography chemical processes and materials differ across 365 nm, 248 nm, and 193 nm technologies.
• Explore lithography simulation tools and understand how modeling is applied to low‑k₁ regimes.
• Work with large semiconductor datasets to identify trends, correlations, and process sensitivities.
• Apply statistical analysis, SPC concepts, and Six Sigma methodologies to evaluate process performance.
• Develop predictive models using machine learning or advanced analytics frameworks.
• Sharpen communication skills through documentation, presentations, and technical discussions with engineers.
• Gain experience transforming data insights into process improvement recommendations.

Deliverable:

Impact of Project:

• Creation of validated analytical models or predictive tools that improve process accuracy and characterization.
• Generation of meaningful insights from photolithography datasets that drive technology decisions.
• Delivery of clear reports, visualizations, and presentations summarizing findings for engineering and management teams.
• Contribution to the development of enhanced data‑driven methodologies for future lithography nodes.

Skillset Required:

• Understanding of optical principles such as immersion lithography, polarized illumination, and low‑k₁ imaging.
• Familiarity with lithography chemicals and materials across 365/248/193 nm exposure technologies.
• Experience using lithography simulation tools for low‑k₁ applications.
• Knowledge of material properties and characterization relevant to lithography.
• Strong foundation in statistics, probability, and data analysis (Cpk, distributions, hypothesis testing).
• Familiarity with machine learning or data science tools (Python, R, or similar).
• Understanding of SPC and Six Sigma methodologies.
• Ability to interpret complex datasets and communicate insights clearly.

Course of Interests:

Engineering (Electrical, Mechanical, Chemical), Data Science, Statistics, Physics, Semiconductor Engineering