DAQ for Sensor R&D at FNAL Ryan A. Rivera 2014 Detector R&D DOE Review 29 October, 2014
Comprehensive Approach to Tracking Detector R&D Creating a tracking and trigger system that can withstand the projected HL-LHC luminosities is perhaps the most important detector challenge in the field of High Energy Physics. There must be a comprehensive approach (emphasis on FNAL/SCD contributions): Radiation hardness in new sensors (3D Sensor columnar + Diamond) New ASIC developments to give Front End integrated track segment information L1 Trigger Track trigger Multi-wavelength optical Data Transmission DAQ FPGA, GPU’s and Track Fitting Associative Memory based on xTCA 2 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Compact And Programmable daTa Acquisition Node (CAPTAN ) • Motivation simple DAQ It is 6” x 6” and, for many systems, the only external connections needed are a 3.5V power supply and a standard Ethernet cable. flexible DAQ The user can stack compatible boards in different combinations to give unique functionality. scalable DAQ In addition to the vertical stacking, the stacks can be repeated arbitrarily and connected with one or many PCs in an Ethernet network. 3 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
CAPTAN User Template • The CAPTAN architecture consists of a few core boards but is intended to be augmented by custom boards designed and built by users. COOLING CHANNEL VERTICAL BUS LATERAL BUS MOUNTING HOLE OPTICAL BUS ELECTRONICS 4 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
CAPTAN: Applications • Developed in 2008/2009, the CAPTAN system was designed to handle common data acquisition, control, and processing challenges within high energy physics. • Examples of such applications are tracker readout systems, R&D test stands, and parallel data processing. As the CAPTAN system is a modular system it can be • used for a wide range of applications, from very small to very large. Quite a number of groups at Fermilab and other • institutes in the US, China, and Europe have acquired the system for their test-stands. We work with them to provide hardware and software support. • We are currently working on the next generation CAPTAN with a new FPGA. 5 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
QIE10 Single Event Upset Testing 6 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Tevatron: T980 Crystal Collimation Telescope 7 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Fermilab Test Beam Facility The CAPTAN pixel telescope is 8 silicon pixel planes leftover from CMS, with space for 2-4 DUTs in the middle. Pixel size is 100 µm x 150 µm. Data acquisition with the CAPTAN system. Pixel Telescope CAPTAN Stacks Power Supply DUT HV Supply Frame Scintillator Ethernet Router 8 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Fermilab Test Beam continued: • Old pixel telescope DAQ is based on CAPTAN • Triggered, 2.5cm 2 coverage, and 8µm track resolution • New strip telescope is based on CAPTAN too. • Dead-timeless, 16cm 2 coverage, and 5µm track resolution • For the last 6 years CAPTAN supported all versions of the CMS pixel chip • Recently tested the VIPIC Read Out Chip from FNAL/PPD using CAPTAN 9 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
FTBF Telescope User Community Telescope is part of the FTBF facility and has been used by many • experiments as a high resolution tracking tool to characterize different Devices Under Test (DUTs) List of Fermi Test Beam Facility Experiments using the Telescope • T992 - Radiation-Hard Sensors for the HL-LHC (ongoing) T995 - Scintillator Muon/Tail Catcher R&D with SiPM Readout T979 - Fast Timing Counters – PSEC Collaboration T1004 - Total Absorption Dual Readout Calorimetry R&D T1006 - Response and Uniformity Studies of Directly Coupled Tiles T1017 - CIRTE (COUPP Iodine Recoil Threshold Experiment) T958 – FP420 Fast Timing Group T1038 – PHENIX Muon Piston Calorimeter We will continue to support test beam experiments that want to use the • pixel telescope 10 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
FNAL/PPD Collaboration • 3D ASIC test stand and test beam efforts, another run in November scheduled. • SCREAM (Single CCD Readout Module): compact low-cost, CCD readout system. Using CAPTAN firmware/software 11 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Collaboration with CMS • New CMS pixel digital ROC Test Stand • Distributed to US collaborators in Colorado, Purdue and to Italian collaborators in Milan, Lecce and Torino. 12 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Leveraging Event Building and xTCA for CMS • Data Processing in an FPGA • Receives all CMS Calorimeter data • 276 Gbps in to a single FPGA (Xilinx Virtex 7) • Must aggregate/summarize information and pass to next stage in < 400ns • 20 Gbps out from FPGA 13 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
xTCA • xTCA (Telecommunications Computing Architecture) Spec put forth by PICMG (PCI Industrial Computer Manufacturers Group: a consortium of over 250 companies). xTCA encompasses MicroTCA and ATCA. • Large experiments are already using xTCA or planning to: – CMS – ATLAS – LHCb – LBNE 14 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
CAPTAN and xTCA • Completed a test beam project at Fermilab Real-time event assembly conducted in MicroTCA form factor. 15 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
MicroTCA effort • MicroTCA.4 Standard Specification finalized in 2012 for the physics community. We are collaborating with CERN to use MicroTCA cards developed in Europe: GLIB, MP7, FC7. 8U 12-slot MTCA.4 shelf 16 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
ATCA effort • ATCA Advanced Telecommunications Computing Architecture • More space for I/O • Possible collaboration with SLAC on RCE development for LBNE • Work led by Ted Liu 12U 14-slot ATCA shelf 17 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Other Data Processing Platforms 18 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
What is artdaq? • A set of applications running extensible software components to be customized by experimenters to create a DAQ system. • Lariat, DarkSide-50, LBNE and Mu2e experiments; partial use in Nova. Chosen for “Off -the- Shelf” DAQ. • Recompilation is not needed in to change parameters – done through configuration scripts that load plug-ins • artdaq-demo allows users to get a toy artdaq-based DAQ system up-and-running from scratch in about 10 minutes 19 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Next Steps for DAQ for Sensor R&D • For CAPTAN: Update FPGA Further develop software on LINUX - web based graphical user interface using HTML5 and JavaScript. Build out artdaq support Exploit parallel processing power and m TCA integration Provide user support for their applications Work with possible users on new applications 20 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Next Steps cont. • For xTCA: Continue to explore big data applications There are possibilities for event aggregating and tracking based trigger systems. • For parallel processing: Compare xTCA with GPU and co- processor fronts Intel PHI CUDA OpenCL 21 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Next Steps cont. • For DAQ systems: Proceed with “Off -the- Shelf” DAQ concept This proposal is intended to demonstrate the feasibility of a low-cost, high-bandwidth, commercial approach to data acquisition based on standard networking technology. We can no longer afford the costs associated with developing new back-end systems from scratch for each new experiment. Experiments are asking for an “off -the- shelf”, commodity solution. The Computing Sector is all about “service” and perhaps it’s time FNAL consider a “DAQ as a service” approach . Effort will leverage CAPTAN, artdaq, and test beam experience. 22 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Conclusion The efforts related to DAQ for Sensor R&D are intimately tied into every piece of the puzzle for a tracking and trigger system that can withstand the projected HL-LHC luminosities . Radiation hardness in new sensors (3D Sensor columnar + Diamond) New ASIC developments to give Front End integrated track segment information L1 Trigger Track trigger Multi-wavelength optical Data Transmission DAQ FPGA, GPU’s and Track Fitting Associative Memory based on xTCA 23 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Thank you. 24 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
Backup slides… 25 Ryan A. Rivera | DAQ for Sensor R&D at FNAL 10/29/2014
CAPTAN Core Boards “Green Board” NPCB – Node Processing “Blue Board” and Control Board DCB – Data Conversion Board “Red Board” PDB – Power Distribution Board
Recommend
More recommend
