Semiconductor Operations Assistant
We built a comprehensive AI-powered operations platform for AstraSemi Corporation that helps semiconductor staff quickly understand operational data, interpret technical messages, and identify semiconductor components. The platform features four intelligent modules: CSV analysis, text interpretation, image recognition, and an interactive glossary — all designed to make complex information accessible to employees at every level.
Role
Full-Stack Developer
Responsibilities
Designed and built the complete web application, including both the React frontend with TypeScript and the Flask backend with Python. Integrated OpenAI APIs for intelligent analysis across all modules, implemented internationalization (English/Korean), and deployed the full stack to production.
Team
- Zhi XunFull-Stack Developer
- KaydenFull-Stack Developer
- MinseoFull-Stack Developer
- SoohaBackend Developer
- MinseokFrontend Developer
- HeechanFrontend Developer
Problem Statement
AstraSemi Corporation's semiconductor operations staff face daily challenges managing diverse information types: shipment spreadsheets, technical messages, equipment updates, and visual inspection data. New employees especially struggle to quickly understand what's important across these different formats. The challenge was to build an AI-powered assistant that could make operational data more accessible, help staff identify semiconductor components from images, and provide clear explanations of technical terms — all in a user-friendly interface that works for both experienced engineers and new hires.
Solution
We designed a modular platform with four specialized AI-powered modules, each addressing a specific operational need. The CSV Analysis module processes shipment and operations data, providing summaries with highlighted anomalies and actionable insights. The Text Interpretation module transforms technical messages into clear summaries with beginner-friendly explanations and can convert them into professional emails or manager updates. The Image Recognition module identifies semiconductor components from photos, explaining what each item is and its role in the manufacturing process. Finally, the Glossary module serves as an intelligent dictionary with AI-enhanced explanations and contextual examples for over 100 semiconductor terms.
Key Modules
- Module 1: Operations Overview Dashboard — CSV analysis with AI-powered insights
- Module 2: Document Interpreter — Technical text simplification and conversion
- Module 3: Image Identifier — Semiconductor component recognition with explanations
- Module 4: Interactive Glossary — Searchable dictionary with AI explanations
Tech
- React 19 with TypeScript and Vite
- Flask (Python) backend with RESTful APIs
- OpenAI API integration (GPT-4o, GPT-4o-mini) for all AI features
- i18next for internationalization (English/Korean support)
- Pandas for CSV data processing
- Production-grade error handling and centralized logging
Platform Modules
Click to explore each module
CSV Analysis
Upload operational CSV files to get AI-powered summaries highlighting key metrics, unusual patterns, and top three action items. Designed for quick decision-making.
Text Interpreter
Paste technical messages to receive clear summaries with beginner-friendly explanations. Convert messages into professional emails or manager updates.
Image Identifier
Upload semiconductor component images to identify wafers, FOUPs, probe cards, and more. Get explanations of what each item is and its manufacturing role.
Interactive Glossary
Search 100+ semiconductor terms with definitions, categories, and AI-enhanced explanations. Includes related terms and real-world context examples.
Implementation Highlights
The platform was built with production-ready practices from day one. I implemented a clean React architecture with TypeScript for type safety, created reusable components for consistency, and integrated i18next for bilingual support (English/Korean). The Flask backend follows RESTful API design principles with proper error handling and logging. Each module was designed to handle edge cases gracefully — from invalid CSV formats to unsupported image types — providing clear user feedback rather than cryptic errors.
One technical challenge was optimizing OpenAI API usage across four different modules while maintaining fast response times. I implemented model selection based on task complexity (GPT-4o for image analysis, GPT-4o-mini for simpler text tasks) and added proper error recovery for API failures. The glossary module uses efficient client-side filtering for instant search results, while the AI explanation feature provides contextual depth when users need it.
Reflection
This hackathon reinforced the importance of building with real users in mind. Rather than creating a technically impressive but unusable system, we focused on solving actual pain points for semiconductor staff. The modular design made it easy to develop and test each feature independently, which was crucial given the tight timeline. I learned to balance feature completeness with time constraints — the four modules cover the core use cases comprehensively rather than trying to do everything.
The project also improved my skills in API integration and error handling. Working with OpenAI's APIs taught me to design robust systems that gracefully handle failures and provide meaningful feedback. The internationalization feature was more complex than expected, requiring careful planning of text structure and context. If I were to extend this project, I'd add user analytics to understand which modules are most valuable, implement caching for frequently accessed data, and expand the glossary with user-contributed terms. Overall, the hackathon demonstrated how AI can be practically applied to domain-specific problems when combined with thoughtful UX design and solid engineering.