1. Optical surveys: sensors for the upgrade Adrian Bevan, Karen Hayrapetyan, Camillia Messiouni 1

2. Overview ROOT is too slow to process large data sets from our smartscope; so have adopted an analysis procedure using Matlab. Fast Felxible Easy to use.... but requires some of us to learn a new tool 2 Mechanicals are flat Sensors are bowed; either convex or concave depending on vendor/process details

3. QA model Collect data in a grid with a finite spacing between measurement points. – Time: 15min for a scan in (x,y) steps of 500 um Plot and fit the data points with a plane. – Time: seconds (gave up on ROOT) Histogram the residuals about the mean defined by the plane; use spread as QA metric. – Acquisition with the OGP software and post processing by Matlab is quick enough that we _can_ assay all sensors passing through our lab before electrical testing. – i.e. can quickly pass/fail sensors out of QA bound of 200um bowing. 3

4. What about residuals for alignment? This is a demonstration – would like to repeat with a module. Use CMS model for alignment of their strip modules as a basis for study. Aim: demonstrate the reduction in residual spread relative to a model. 4 Flat sensor orientation plane dfn. Parabolic bowing term Linear bowing term ATLAS equivalent; only use the first 3 terms and fit for 4 sensors glued in an ideal module. See: NIM A 650 240-244 (2011) (v)

5. Results with a mechanical sensor Fitting with a planar model 5 45-50um spread for residuals Residual (data - fit model) Number of points

6. Results with a failed electrical sensor (1) Fitting with a planar model 6 150um spread for residuals Residual (data - fit model) Number of points

7. Results with a failed electrical sensor (2) Fitting with a planar model 7 200um spread for residuals Residual (data - fit model) Number of points

8. Results with a mechanical sensor Fitting with the CMS model 8 30-35um spread for residuals Residual (data - fit model) Number of points

9. Results with a failed electrical sensor (1) Fitting with the CMS model 9 ~45um spread for residuals (c.f. 150 for planar model) Residual (data - fit model) Number of points

10. Results with a failed electrical sensor (2) Fitting with the CMS model 10 20-40um spread for residuals (good description of most points, but long tails) Residual (data - fit model) Number of points

11. Results with a mechanical sensor Fitting with cubic terms 11 30-40um spread for residuals Residual (data - fit model) Number of points

12. Results with a failed electrical sensor (1) Fitting with cubic terms 12 40um spread for residuals Residual (data - fit model) Number of points

13. Results with a failed electrical sensor (2) Fitting with cubic terms 13 25-30um spread for residuals Residual (data - fit model) Number of points

14. Overview of results Three fit models used: – Planar (linear) – Quadratic surface – Cubic surface – At least a factor of 3-5 improvement in the residuals measured for these sensors relative to a nominal planar test when applying a more sophisticated model. – Best case: is a x5-10 improvement for convex bow. – More statistics required to draw conclusive results. – We care about modules, not sensors, for the build; so need to repeat with modules. 14 Spread in Residuals Planar fitQuadratic fitCubic fit Mechanical45-5030-3530-40 Concave bow1504540 Convex bow20020-4025-30

15. Local plank flatness (Stave Plank 7) Is the surface flat (if so how flat)? – on the scale of a module... 15 Did a laser surface scan over a region of the stave (100x100mm). Generally flat – can see undulations from tracks; LHS is the main power line track that sticks out of the surface (old design of the kapton bustape). Flat to better than +/- 50 um. Caveat: old tape design, new planks should be a lot better than this!

16. Summary Can reduce the hypothetical position error by a significant factor: – x3-5 with these sensor tests. – Want to repeat with modules to quantify how this would affect the real items going into the SCT / pixel system. Also plan to do cooling tests: exploring the possibility of having this as part of an ATLAS authorship task for a student. 16