1. Electrical Engineering at Fermilab The Hidden Agenda Behind All This Physics Stuff

2. Jim Hoff and Farah Fahim (Jim got too much credit on the poster) Presented by:

3. Engineering versus Physics…what’s the real difference? Engineering Physics Engineers build machines. If, along the way, they happen to uncover some phenomena or help other people to do so…oh well, that was fun. Physicists pursue phenomena. In order to do so, if they have to build some machines, that is just the cost of business

4. That being said… Engineering is better Electrical Engineering is MUCH better.

5. For the rest of the talk There are lots of types of electrical engineering at Fermilab…  Power Engineering  RF Engineering  Board Design  Etc…etc…etc… For the remainder of the talk, we’ll focus on Front End Electronics and Integrated Circuit Design Engineering, largely because it is widely regarded as the best, most significant and most interesting type of electrical engineering, but also because it is what we do. No one said we couldn’t be biased in this presentation…

6. What does a Physicist see? Physicists pursue phenomena so they SEE phenomena. They see particles and their interaction.

7. What does an Engineer see? Engineers see the machines. We see the hundreds and thousands of little detectors.

8. What does an Engineer see? Engineers see the machines. We see the hundreds and thousands of little detectors. We see tiny puffs of charge that “magically” appear at the inputs of our electronics. On some level we really don’t care where they come from. It is also significant that, at least at first, there is NO ORDER to what we find and there can be a LOT of noise. Order must be extracted and noise must be suppressed.

9. What does an Engineer see? Engineers see the machines. We see the hundreds and thousands of little detectors.

10. “Tiny Puffs of Charge”? Really? For example: LAr Detectors like LBNE 10000 electrons Pixel Detectors in CMS 1000 electrons CCD Detectors in CDMS A few electrons

11. What do we do with these tiny puffs of charge?

12. Limitations GeometrySizeNeighborhoodTimePower How do we get this done?

13. Shut up and let Farah talk…

14. Remember this?

15. This is where we start… Every 25ns… Most of these events are meaningless, and the amount of information gathered is staggering, so we have to discard most of it. Still, when we find something interesting, we have to turn this…

16. Into this… We have to extract the significant particles from the meaningless ones and from the noise.

17. How? The desire for high momentum tracks allows us to narrow the scope to a set of towers Simulations prior to experimentation allow us to predict patterns of hit detectors that indicate a significant track amid all the noise.

18. Real Time Track Finding For simplicity, we will look at this in 2 dimensions rather than 3. Layers correspond to, for example, each set of concentric cylinders within the tracking detector. Imagine simulating all conceivable tracks within this space and then recording those tracks in a Pattern Recognition Associative Memory.

19. What is a Pattern Recognition Associative Memory? Ordinary read-only memories respond to a new address presented at its inputs with the data corresponding to that address. Someone gives it an address and the ROM responds with data. Simple. Associative Memories respond to data with data. A single piece of data given to an associative memory could result in several associations or it could result in none.

20. What is a Pattern Recognition Associative Memory? Pattern Recognition Associative Memories take it one step further. Data is first subdivided into categories. For example, hair color, eye color, height and weight. Data is only matched within category. For example, hair color data is only matched against hair color patterns. Once a match is found in each category, we have found a potentially interesting pattern.

21. Pattern Recognition in HEP Our categories are detector layers. Our data are detector addresses within each detector layer. Given a pattern recognition associative memory with enough patterns to cover the tower and with the speed necessary to match patterns in the time allowed, we can do the job.

22. High-Speed Pattern Recognition Layer 1 Address 4 Match Layer 1 Address 4 Match Layer 3 Address 7 Match Layer 3 Address 7 Match Layer 3 Address 9 Match Layer 3 Address 9 Match Layer 2 Address 1 Match Layer 2 Address 1 Match Layer 4 Address 4 Match Layer 4 Address 4 Match Layer 2 Address 4 Match Layer 2 Address 4 Match Road!

23. The CAM Cell In fact, a direct implementation of the figure on the preceding page proved to be possible and it is shown here. To the left is a floorplan of the layout and to the right is the layout itself. This implementation brings out several features of the VIPRAM not immediately obvious. First, unlike the classical 2D PRAM architecture which is in a straight line, the resultant square layout of the 3D VIPRAM permits routing of signals from left, right, top and bottom. Second, the matchline of the CAM cell itself is shortened. In the TIPP paper, we talk about the shortening of the Stored Matchlines (Page 7, below Figure 4) and indicate that this will reduce power. Frankly, we were wrong. The Stored Matchlines do not change state rapidly, so they don’t draw much power. However, the CAM match lines run at 100+ MHz, and reducing their parasitic capacitance dramatically reduces the system power consumption. None of this was disclosed publicly at TIPP. matchLine

24. The Control Cell (Majority Logic) A direct implementation of the Majority Logic as shown on Slide 9 is also possible. To the left is a floorplan of the layout and to the right is the layout itself.

25. Final 3D Implementation

26. Conclusions Engineers build machines, and the accelerators and detectors in HEP are among the most complex machines in history. In fact, these machines are themselves composed of smaller machines that are, each in their own right, enormously complex. All joking aside, this place is an engineer’s playground.