SemiAcademy micro-credential
AI for Semiconductors
Machine learning applied to semiconductor data, with hands-on Python labs that run in the browser and a capstone project.
At a glance
- Time needed
- About 73 hours, at your own pace
- Level
- Intermediate
- Who it is for
- College students and early-career engineers
- Prerequisites
- None. No prior background is required; Module 0 is a Python jumpstart.
- Format and what you need
- Online and self-paced; a web browser. The coding labs run in the browser, in Colab notebooks.
- Badge
- AI for Semiconductors digital badge
How the course is organized
- Before You Begin — Pre-Program Survey
- Start Here — Orientation
- Module 0 — Environment and Python Jumpstart
- Module 1 — Semiconductors from Sand to System
- Module 2 — Where the Data Comes From
- Module 3 — Machine Learning Foundations
- Module 4 — The Metrics That Matter When Defects Are Rare
- Module 5 — Deep Learning and Vision for Inspection
- Module 6 — Learning Without Labels
- Module 7 — Sensors, Time, and Equipment Health
- Module 8 — AI in Design and Test
- Module 9 — Where the Compute Comes From
- Capstone
- Course Completion
How to earn the badge
- Score 80% or better on every module’s knowledge checks and module exam.
- Clear the capstone gate at twelve of twelve — nothing is marked below that.
- Submit the capstone proposal, notebook, model card and memo .
The capstone gate is pass-or-not on every one of the twelve points. It is deliberately unforgiving, because the thing being certified is whether you can tell a real result from an impressive-looking one.