Yield and Quality Issue Solving by Correlating Optical Inspection Step Results With Electrical Tests (2023-EU-PO-1343)In the semiconductor manufacturing industry for automotive, parts are tested at each manufacturing step to screen likely-to-fail parts. The further upstream the weak parts are scrapped, the lower the scrapping cost will be. But testing has a cost as well. A recent project at NXP sought to avoid a manual defect classification of the defects observed at the wafer inspection level. Defects are now classified as killer or not-killer from a training image dataset, and a failure probability is assessed for each die. JMP® allows a further step in correlating this failure probability to electrical tests with three types of analysis. The first analysis assessed a failure probability threshold to limit the number of parts tested to limit test cost. The second analysis highlighted the tests most correlated with failure probability. The final analysis used the list of highlighted tests to adjust test limits to screen the parts with failure probability outliers. The analyses limit test costs while increasing quality.
Automated Process of Collecting Product Test Data and Creating Alarm Reports for Root Cause Analysis (2023-EU-30MP-1268)One of the most important product test machines (ATOS) is investigated in this global Autoliv project with the target of introducing an automated alarm system for product test data and a root cause analysis. We wanted a flexible automated software solution to transfer data into an SQL database and perform a root cause analysis. Furthermore, we wanted to send web-based links of reports to an existing “leading-to-lean” (L2L) dispatch system, which informs machine owners via mail. We use JMP to automate all processes via Task Scheduler for all these tasks.
Pythonless Python Integration for JMP® (2023-EU-30MP-1265)
Challenges with a JMP® and Python integration resulted in a search for an alternative solution that would allow for the evaluation and testing of the various Python libraries and powerful algorithms with JMP. This would enable JMP users to work with Python from a familiar JMP environment. After a few different iterations, a RestAPI service was developed, and when JMP calls this service, it dynamically creates a user interface based on the options the service currently provides. The JMP user can then utilize this user interface to employ different algorithms such as HDBSCAN, OPTICS, and UMAP by sending data directly from JMP in one click. After the algorithm has finished its operations on the server side, it will return data to JMP for further analysis and visualization.
