As manufacturing in the USA becomes increasingly automated by artificial intelligence (AI), semiconductor chip manufacturing hasn’t yet seen widespread AI automation. Currently, semiconductor manufacturing research requires human operators to perform tasks, both in the lab and at the factory. Adding to the problem with chip manufacturing, defect formation in some types of semiconductor chips can ruin their performance, but defects can typically be detected only after the process has been completed. At that point, the chip must be scrapped or repurposed. This problem is especially challenging with promising materials such as diamond, which could transform power transmission and other advanced applications due to its unparalleled thermal and electrical properties. This research endeavors to use AI monitoring together with innovative defect detection and process analysis to automate these processes. This work could lead to an entirely automated cybermanufacturing approach to semiconductor chips, using diamond as the flagship material. The work also includes objectives to assist the existing and future manufacturing workforce to understand, learn, and adapt to AI processes, so as to translate existing jobs into next-generation Industry 4.0 careers. The research will be integrated into a forward-thinking training program in semiconductor manufacturing for graduate students and senior-level undergraduates at MSU and local community colleges, as well as events and opportunities for