Key Takeaways
- Engineers leverage both device-specific and tool-level data to identify a process “sweet spot.”
- Tight, frequent tool-to-tool matching enables greater yield and fab flexibility.
- Machine learning helps capture the nuances of a tool’s signature.
Many people outside of the semiconductor industry wonder how humans can fabricate transistors with tens of nanometer scale dimensions on a consistent basis, day in and day out, from one process tool to another, one line to another, and one fab site to another. One way this is achieved is through tool-to-tool matching (TTTM). But TTTM is getting much more difficult as fabs produce increasingly complex chips with smaller features and process windows.
Wafers can undergo 600 to 800 process steps over the…