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Department
Heterogeneous Integration Group, High Bandwidth Memory Product System Engineering
Project Title
AI-Enabled Unified Component Validation Analytics Platform Using Tableau
Project Description
The intern will develop an AI-enabled analytics platform that transforms raw component validation data into standardized and interactive visualizations using Tableau.
Through this project, the intern will gain practical experience in semiconductor validation analytics, data engineering, dashboard development, and Artificial Intelligence applications. The platform will enable engineers to examine High Bandwidth Memory validation results across different products, operating conditions, validation flows, and execution runs.
Objective of the Project
Develop a centralized and scalable analytics platform for Component Validation data.
Establish a reusable data model that enables consistent analysis across validation flows and High Bandwidth Memory product generations.
Improve the clarity and accessibility of validation results through interactive Tableau dashboards.
Evaluate AI-enabled methods for anomaly identification, root-cause investigation guidance, and generation of engineering insights.
Opportunities for Full-Time Employment
The internship will provide exposure to engineering roles involving product validation, data analytics, software development, and Artificial Intelligence.
Any consideration for future employment will be based on business requirements, position availability, and the intern’s overall suitability.
Project Scope
Develop or enhance a centralized Tableau dashboard platform for Component Validation analytics.
Create reusable Python or Java data-processing workflows to ingest, clean, normalize, and integrate execution data from multiple validation flows.
Design a standardized relational data model for cross-flow and cross-product analysis of High Bandwidth Memory validation results.
Build interactive dashboards containing plots, tables, trends, statistical summaries, and comparisons between current and next-generation products.
Explore AI-enabled and Agentic AI approaches for anomaly identification, root-cause investigation guidance, workflow scalability, and generation of interpretable engineering insights.
Learning Opportunities
Gain practical knowledge of High Bandwidth Memory product validation and semiconductor engineering data.
Develop proficiency in Tableau dashboard design, data storytelling, and visualization best practices.
Apply Python or Java and Structured Query Language to data processing, integration, and reusable pipeline development.
Learn to evaluate Generative AI, AI Assistants, Large Language Models, or Agentic Solutions within an engineering analytics context.
Deliverables
A scalable, interactive, and user-friendly Tableau platform for Component Validation analytics.
Standardized data structures and documented data pipelines for repeatable dashboard development.
Visualizations of validation trends and product comparisons across voltage, temperature, data rate, validation flow, and execution runs.
A technical project report documenting the architecture, development methodology, findings, limitations, and recommended enhancements.
A final presentation and demonstration of the completed platform and project outcomes.
Impact of the Project
Improve the consistency and accessibility of Component Validation analytics.
Enable more effective comparison of validation results across products, conditions, and…