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Development of the Optical Communications for the Tmb-2025 Board for CMS Muon Trigger Improvement Project at the LHC
Get PDFThe Large Hadron Collider (LHC) is the largest and highest energy particle accelerator in the world, colliding two proton beams together at 13.6 TeV center-of-mass energy. The Compact Muon Solenoid (CMS) is one of the large general-purpose detectors used in the LHC to study what happens in each collision. CMS consists of several sensor arrays that collect a vast amount of data during each collision. To maintain a manageable amount of data readout bandwidth, a trigger system is designed to quickly and efficiently identify interesting physics events and reject background. The Trigger Mother Board (TMB) was designed to be an integral part of the trigger system in CMS. The CMS was built in the early 2000s, and several upgrades were made over the years. However, some original TMBs from 2005 (TMB-2005) boards have never been upgraded. The primary objective of the TMB-2025 project is the substitution of the original TMB-2005 with a new board using an updated Field-programmable gate array (FPGA) characterized by better logical capabilities and support for a multi-gigabit optical communication system. The optical communication system includes FPGA and firmware, the signal transmission through the printed circuit board (PCB), and optical transmission through a newly adopted Samtec Firefly optical transceiver. It is crucial to develop validation test procedures to verify that the optical communication system implemented on the PCB operates reliably in the conditions required for CMS. For this purpose, the optical transceiver is configured for loopback operation, with outgoing fiber transmitter data coming back into the system through corresponding fiber receivers. A custom firmware is designed and loaded to the Virtex-6 FPGA, allowing the transmitters to be configured for sending randomized binary transmission test patterns. The pattern checker logic is implemented in the receiver, functioning to count errors in the received data patterns. Xilinx software tools are used to control the transceiver and to access the error counts from the checker logic. The developed firmware is validated and is able to measure the reliability of the optical communication system. It can be adapted for future use in automated testing of optical parts received from the factory, as well as PCB production testing and quality control for TMB-2025 boards. The measurement of the optical communication system, which contains the newly adopted optical transceiver and PCB design, demonstrates a very high level of reliability and validates the concept for the optical communication system of the TMB-2025 project
Impacts of a Changing Environment on Floods, Droughts, and Surface Water Availability in Texas Watersheds
No full textThe main purpose of this dissertation is to study the water resources challenges through a multifaceted framework that includes different factors, such as urbanization, climate change, and dwindling water resources. Employing a combination of in situ observations, remote sensing products, hydrological modeling, and water management modeling approaches, three studies are being carried out over three representative Texas river basins in this dissertation.
The first study focuses on quantifying the climate change and urbanization impacts on floods. The impacts from the changing climate and from urbanization are separated through a hydrological modeling framework over two adjacent river basins in Texas. The main findings include: (1) Urbanization can reduce lag time and elevate flood peaks significantly; (2) When there is little land cover change, changing climate is the major driver of variations in the monthly peak flow; (3) Fast urbanization can amplify streamflow variability, increase peak flow significantly, and alter the timing of change point.
In the second study, we apply the integrated climate-hydrology-management (CHM) modeling framework with two modeling approaches���the Distributed Hydrology Soil Vegetation Model (DHSVM), and Water Availability Modeling (WAM). Our major findings are: 1) Both gross and net evaporation rates are projected to increase from near-future to far-future. 2) The firm yield values of all reservoirs are projected to decrease. 3) Under climate change, the water users in the study region are very likely to experience inadequate water supply in the future.
The third study introduces a Modeling Assisted Drought Analysis (MADA) framework to assess hydrological drought and its propagation from meteorological drought. Our major findings include: 1) Both severe and extreme droughts display shorter durations in the future than the history. 2) The differences of the total drought duration between the future and the history are more significant for extreme droughts than for severe ones. 3) The multi-model averaged drought propagation time vary from one to nine months among all cases. There is a high likelihood that drought propagation time is expected to decrease in the future
Streamlining TNS Data Collection for ML-Based RTL QoR Prediction
Get PDFChip designs must meet several requirements before they are ready for fabrication. One of these requirements is achieving convergence on timing (frequency). Meeting this requirement is a time-consuming task for chip designers in the industry for two reasons. First, the standard approach to procuring this metric involves running logic synthesis and placement, both of which can take hours to weeks on larger RTL designs. Second, since the timing requirement is rarely met after one design iteration, these processes need to be rerun multiple times to recalculate the metric to ultimately converge on the design���s requirements. A critical measure of timing convergence is the total negative slack, commonly referred to by its acronym TNS. It indicates the sum of timing margins of all ���negative slack��� paths that fail to meet the target clock cycle time. To expedite design convergence, our research team previously presented a machine learning-based approach to estimate the TNS values for chip designs expressed in Verilog hardware description language. This technique was orders of magnitude faster than running logic synthesis and placement on those same chips. In this work, we build on the previous approach by improving the initial data generation process. Getting ���true��� TNS values for training the machine learning models involves running logic synthesis and placement with hundreds of synthesis recipes for each design, resulting in tens of thousands of synthesis and placement runs. Driven by the need to create a rich training data set, since new designs will be continuously added to the RTL developer���s set of training designs, it behooves to reduce the number of synthesis and placement runs necessary to generate machine learning (ML) training data. By taking advantage of similarities in the distributions of TNS values across chip designs, the number of required synthesis and placement runs for n Verilog RTL designs and m unique synthesis recipes can be reduced from O(nm) to O(n+m) without meaningfully compromising the integrity of the training data and the accuracy of ML predictions. We present two methods for achieving this, both of which involve finding the common TNS distribution, then normalizing and computing missing values in the data set. The discoveries made by our research team have the potential to drastically reduce the time to market for a variety of semiconductor computing products, including but not limited to graphics processors, motherboards, and flash memory
Assesment of Carbonates as CO2 Utilization Options
No full textThe urgent need for a sustainable carbon-free society requires strategic approaches to address the environmental and economic challenges associated with implementing Carbon Capture Utilization and Storage (CCUS) technologies. This work proposes a streamlined approach to process system engineering to develop cost-effective Carbon Capture and Utilization (CCU) pathways. The high costs related to implementing the CCU technologies discourage investments towards it. Despite this, the importance of a strategic framework and proper research allocation is required in order to implement optimal solutions for reducing carbon dioxide (CO2) emissions in the atmosphere. The proposed approach serves as a methodological tool to systematically identify and implement profitable routes in CCU. As many possible choices exist, a need to develop a strategy that considers these choices for CO2 capture is crucial. The work presents the choices in a simplistic graphical format that can be interpreted easily by people in the industry. In addition, these graphs will be used to help determine the most cost-optimal CO2 reduction pathways. Recently, a new method has been proposed to perform high level analysis of CO2 reduction by developing integrated minimum marginal abatement cost curves (Mini-MACs). The methodology of our work involves the development of these curves for different carbonates including sodium bicarbonate and dimethyl carbonate. The Mini-MAC curves will be analyzed to determine the lowest cost solutions available for these different carbonates and recommend the most feasible options. In addition, the method will be applied and analyzed on a case study that mainly focuses on the potential of implementing these solutions in the state of Qatar
