1 / 19

Predictive Maintenance

Predictive Maintenance. 張智星 (Roger Jang) jang@mirlab.org http://mirlab.org/jang 台灣大學 資訊系 MIR 實驗室. Problem Definition. Given a set of training data Input: A set of wafers with their sensor readings Output: Maintenance stage (1-3) Goal Construct a model to predict maintenance stage.

candice-black
Télécharger la présentation

Predictive Maintenance

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Predictive Maintenance 張智星 (Roger Jang) jang@mirlab.org http://mirlab.org/jang 台灣大學 資訊系 MIR實驗室

  2. Problem Definition • Given a set of training data • Input: A set of wafers with their sensor readings • Output: Maintenance stage (1-3) • Goal • Construct a model to predict maintenance stage

  3. Important Statistics • Specs. of the problem • No. of recipe steps = 10 • No. of sensors = 57 • Statistics for a step: • mean, min, max, std, slope (mean is used here) • No. of wafers = 47 • By machines at different maintenance stages • No. of maintenance stages = 3 • Specs. of classification • No. of instances = 47 • No. of features = 10*57*5 = 2850 • No. of classes = 3

  4. Average of 57 Sensor Readings • 47 curves for each subplot, 10 points for each curve

  5. LDA Projection to 2D Plane for Each Sensor • LDA for dimensionality reduction (10  2)

  6. Best Performance For Each Sensor • Best LDA projection for a single sensor

  7. Confusion Matrix for the Selected Sensor

  8. Comparisons • Result of prediction Groundtruth: Predicted:

  9. Application Case 2 • Characteristics • Different set of recipes • More wafers

  10. Important Statistics • 5 Recipe steps • Stabilize, strike, nitridation, dechuck, purge • No. of sensors = 50 • Used statistics = mean • No. of wafers = 1522 • No. of maintenance stages = 3

  11. Best Performance For Each Sensor • After best LDA projection from 5 inputs

  12. Best Perf. For All 435 Sensor Pairs • After best LDA projection from 5*2 inputs

  13. Best Perf. For Top-100 Sensor Pairs • After best LDA projection from 5*2 inputs

  14. Confusion Matrix and Error Positions • Confusion matrix • Error positions

  15. Future Work • To explore further: • Which recipe is the most influential? • Within the most influential recipe, which sensors are the most influential? • How to further explore feature extraction/selection? • Machine-dependent modeling?

  16. Thank you for your listening!

  17. Application Case 3 • Characteristics • 9 maintenance stages

  18. Best Performance For Each Sensor • After best LDA projection from 5 inputs

  19. Best Perf. For All 435 Sensor Pairs • After best LDA projection from 5*2 inputs

More Related