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Development of ML-based CTQ Define Add-in (2022-KR-30MP-1361)

Jaejoo Choi , SK Hynix

 

"The test process generates the most data in the company, but there was a limit to the use of the data due to the lack of tools that could handle the data smoothly. In particular, the data generated from the wafer test process reflects the quality level of the fab, but it was only partially utilized by engineers. In order to increase the utilization of test data and contribute to yield/quality improvement, we developed a user-friendly CTQ Define Add-in that derives the CTQ of wafer test by grafting the ML algorithm supported by JMP. In this presentation, we will introduce a series of analysis processes that define CTQ for each target based on the add-in and derive optimal coefficient conditions for field application targeting CTQ items.”Breakout