Intern - TD Wet Process Data Analytics and Process Optimization

Location: F10 Singapore
Department: New Technology Introduction (NTI), Micron Technology Singapore
 

Project Overview

The New Technology Introduction (NTI) organization in Micron Singapore focuses on advancing process technology development for new memory technology nodes prior to manufacturing deployment. This internship is structured as a project‑based learning assignment centered on applying data analytics and AI‑based modeling techniques to wet etch and wet clean processes used in advanced memory fabrication.

The project provides exposure to how modeling, experimentation, and data‑driven insights enable process margin identification, failure mode understanding, and accelerated technology learning during development phases.

Learning Objectives
Through this internship project, the intern will:

• Gain foundational understanding of semiconductor wet etch and wet clean process technologies
• Learn how AI and data analytics are applied in process technology development environments
• Develop skills in experimental planning, data interpretation, and model validation
• Build technical communication skills through structured documentation and presentations
• Understand cross‑functional interactions involved in technology development and manufacturing readiness

Project Scope & Key Responsibilities

The internship project covers the following technical areas:

• Development and optimization of data‑driven and AI‑based models for wet etch and wet clean processes
• Identification of process margins and potential failure modes during technology learning phases
• Planning and execution of design of experiments (DOE) to generate structured datasets
• Analysis of experimental data using critical dimension (CD) measurements, scanning electron microscopy (SEM), and other wet process characterization techniques
• Evaluation of model accuracy through correlation of predictions with experimental outcome
• Exploration of process enhancement opportunities to meet defined structural and electrical criteria

Deliverables

At the conclusion of the internship, the intern is expected to deliver:

• AI or data‑driven models addressing defined wet process development objectives
• Experimental plans and summarized results from completed DOEs
• Data analysis reports correlating process behavior with model predictions
• Recommendations for process improvement or optimization opportunities
• Final project presentation and technical documentation

Skillsets Required

• Strong analytical and logical problem‑solving ability
• Basic knowledge or interest in data analytics, modeling, or machine learning techniques
• Familiarity with experimental data analysis and interpretation
• Clear written and verbal communication skills
• Ability to manage multiple technical tasks within a structured project environment

Course of Interest

• Candidates must be currently pursuing a full‑time undergraduate degree in Chemical Engineering, Materials Science and Engineering, Electrical Engineering, or Mechanical Engineering.
   

Duration / Period

• Minimum 5 months, Semester Internship: August 2026 to December 2026