High-Luminosity LHC Upgrade
Boosting Reliability: AI for ATLAS Tile Calorimeter LVPS Production
The ATLAS Tile Calorimeter, a critical component of the High-Luminosity LHC, demands unprecedented reliability from its Low Voltage Power Supply (LVPS) system. This analysis explores advanced quality control measures, including machine learning, to detect early failures and ensure robust operation amidst increased radiation and luminosity.
Executive Impact & Key Metrics
The upgrade of the ATLAS Tile Calorimeter's LVPS system is crucial for the High-Luminosity LHC. Implementing a comprehensive testing and machine learning strategy promises significant improvements in system reliability and operational efficiency, directly impacting the detector's performance and data integrity.
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Target LVPS Efficiency
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Total LVPS Boards Produced
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Hours per Burn-in Test
Deep Analysis & Enterprise Applications
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The TileCal LVPS Phase II Upgrade addresses the challenges of increased luminosity and radiation by replacing existing electronics with higher radiation-hard components. The new system converts 200V to 10V DC, featuring a custom-built, compact, water-cooled design targeting 75% efficiency and operating at 10V, 2.3A in magnetic fields. This transition from a 2-stage to a 3-stage powering distribution system includes new point-of-load regulators to accommodate upgraded on-detector electronics.
A rigorous quality control procedure is in place, involving multiple test benches at the University of the Witwatersrand and Jemstech. This includes visual inspection, initial functional testing, and burn-in testing to identify infant mortality failures. Machine learning is integrated to detect deviations from normal behavior, ensuring boards operate within design specifications before shipment to CERN. Each board undergoes 11 tests, all of which must pass for qualification.
Machine Learning (ML) is being employed to identify early failure risks in LVPS boards. By analyzing functional parameters from test stations, ML aims to detect subtle deviations from normal operational behavior. A shallow neural network (NN) with one hidden layer is trained on pre-production data (88 rows, 54 columns) to assess if 'before' and 'after' burn-in data can be distinguished. While initial results show less variability than expected post-burn-in, further refinement of ML models is ongoing to enhance prediction capabilities.
LVPS Production Progress
0 Pre-production LVPS Boards Manufactured in South AfricaEnterprise Process Flow
Boards Manufacturing
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Visual Inspection
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Board ID Attached
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Initial Testing
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Burn-in Testing
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Final Testing
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Ship Boards to CERN
| Parameter | Value | Significance |
|---|---|---|
| Controller Chip | LT1681 (~300kHz switching) | Heart of design, high-frequency operation for efficiency. |
| Over Voltage Protection | 11.5 - 12.5 V | Prevents damage from excessive voltage, crucial for detector safety. |
| Overcurrent Protection | 6.75 - 10.75 A | Safeguards against component overload and potential burnout. |
| Output Voltage Ripple @ NL | < 0.5 V (p2p) | Ensures stable power delivery to sensitive front-end electronics. |
| Efficiency @ NL | > 65% (Target 75%) | Minimizes power loss and heat generation, important in radiation environments. |
| Over Temperature Protection | 70 Degrees C | Protects the board from thermal runaway during long-term operation. |
Impact of Burn-in Testing on LVPS Reliability
The burn-in testing phase is critical for identifying infant mortality failures before shipment. This process involves subjecting boards to increased temperature and load, simulating long-term operation to accelerate aging. For example, during pre-production, 104 LVPS boards were manufactured, and the burn-in test takes 8 hours for each board. Observations include various failures such as burned capacitors and inductor solder cracks, which are detected and addressed during this crucial phase. The graph on slide 11 shows the cumulative production of LVPS boards, demonstrating a steady ramp-up in tested and qualified units, reinforcing the importance of this rigorous testing protocol for overall reliability.
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Your AI Implementation Roadmap
Our roadmap outlines key phases for the successful deployment and integration of the upgraded LVPS system, from continued production to final installation and operational monitoring at CERN.
Continued Production & Testing
Ramp-up full production of 1032 LVPS boards, with daily testing of 50 units across two stations. Ongoing refinement of ML models based on production data.
Shipment & Installation
Packing boards in boxes of 75 units (3 layers of 25) and shipping to CERN. Begin installation into the ATLAS Tile Calorimeter modules during LS3.
Detector Integration & Commissioning
Integration of the new LVPS system with the upgraded on-detector electronics. Comprehensive commissioning tests to ensure full compatibility and performance.
Long-Term Monitoring & Maintenance
Continuous monitoring of LVPS performance during HL-LHC operation, leveraging collected data for predictive maintenance and future improvements in data-taking reliability.
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