1. Interaction of Process data and Non Productive data – a general approach Dr. A. Behrisch a, J. Zimpel b a Infineon Technologies AG b Advanced Data Processing GmbH Dresden 6. European AEC/APC Conference Dublin / Ireland, April 6.-8., 2005 Besser aufbereiten Schichtenmodell - Verenglischen und KommentaR UNTEN Conclusion M-PCA Decomposition of Trace data: Gas flows and pressure during handling Application of Hotelling T 2 Temperature during Deposition processes and Cleans Correlation Analysis Deposition processes and Etch back  Within the last years, fault detection and classification (FDC) based on machine data (internal sensors) and external sensor data became an essential part of the production processes.  There are generally two different ways to group the machine data for analysis, classification and supervision. -Equipment perspective (used mostly by Unit process- and Maintenance- organizations) -Lot/Wafer perspective (used mostly by Process integration-organizations)  Within the Infineon APC project in the past, the main focus was put on the equipment perspective from which most of the machine failures and production time losses can be detected.  Now the focus of FDC starts to shift towards the lot/wafer perspective which opens up a completely different view on dependencies between machine data and metrology data / process results.  Also it is very important not only to analyze, classify and supervise machine data during production but also in between production runs (cleans, tests, machine checks, conditioning).  The combination of the two additional aspects (analysis of non-productive machine data and lot perspective) leads to the approach of machine data analysis presented in this publication. Introduction Data analysis Tool A Tool C time time KeyNumber x Tool B time Analysis of equipment data using  Time series analysis  WER  Outlier detection (Limits, T2, Q) Clean ...  Metrology Yield  elec. Para Run Sequence Process 1 Clean 1 Process 2... Metrology Elec. Para … Yield data, … Analysis of dependencies using:  Correlation Analysis  Modeling  Decomposition  graphical projection time.... Channel n.. Recipe Step.. Run #1Clean Run #2Run #1........ Run #last Measurement Channel time t /s Run #1 Run #2 Run #last Channel 1 Channel 2 Channel 4 Channel 3 Measurement Channel time t /s Run #1 Run #2 Run #last Channel 1 Channel 2 Channel 4 Channel 3.... Channel n.. Recipe Step.. Run #1Clean Run #2Run #1........ Run #last time.... Channel n.. Recipe Step.. Run #1Clean Run #2........ Run #last time t /s Run #1 Run #2 Run #last time t /s Run #1 Run #2 Run #last Run #1 Data acquisition Productive data Productive data including handling Productive data and Non Productive data including handling Equipment Perspective Lot Perspective 1) Different offset levels due to different cleaning cycles. 2) Some Runs show a different stabilization speed. 3) and 4) Variation in the shape of the curves can be detected. 1)Temperature control problems for single runs 2)Different temperature behavior within several cleans (partial correlation with 3) 3)Temperature changes earlier - compared to the other runs within the same lot 1) 2) 3) Productiv Clean  Trace data was aggregated to summary data per Run.  Correlation analysis mainly shows correlations between summary data coming from the same process chamber and not in between deposition and etch back (processes independent).  Main focus of the analysis of aggregated machine data is to detect machine and process instabilities and not to focus on the interaction between production processes. Probably data acquisition and aggregation methods are not yet adapted to show these interactions. Deposition Etch back 1) 2) 3) 4) Channel 1 = H2_M Channel 2 = H2_SP Channel 3 = DOP Channel 4 = CHPR  This poster presents a different view on the machine data available besides the traditional focus of FDC.  Machine data recorded during non productive times provides information which sometimes can not be achieved from the machine data during production.  a FDC System should provide the possibility to gather and analyze data of the whole process including handling, cleans, tests,...  Machines’ non productive data can be aggregated and analyzed with the same methods like productive data.  Nevertheless a correlation between productive data - non productive data, pre process – post process is not that easy to detect. Process