Enabling process control though predictive design and virtual metrology for high product mix manufacturing

The semiconductor foundry industry faces challenges in managing diverse customer demands and complex manufacturing processes. Variations in the chemical vapor deposition process affect transistor parameters and yield. Siemens' Calibre® software with machine learning techniques create a virtual...

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Bibliographic Details
Published in2024 8th IEEE Electron Devices Technology & Manufacturing Conference (EDTM) pp. 1 - 3
Main Authors Lee, Hyung Joo, Choi, Sanghyun, Krishnankutty, Sudheesh, Botta, Raghavendra, Greeneltch, Nathan, Jayaram, Srividya
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.03.2024
Subjects
design features
Foundries
high product mix manufacturing
machine learning
Manufacturing processes
Metrology
Process control
Semiconductor device modeling
Simulation
Software
virtual metrology
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Summary:The semiconductor foundry industry faces challenges in managing diverse customer demands and complex manufacturing processes. Variations in the chemical vapor deposition process affect transistor parameters and yield. Siemens' Calibre® software with machine learning techniques create a virtual metrology model that outperforms traditional methods. An advanced process control system, incorporating design features and real-time data, improves process capability and reduces film thickness variations in high-mix product foundry fabs, as confirmed by control simulations.
DOI:10.1109/EDTM58488.2024.10511671