Environmental-Based Characterization of SoC-Based Instrumentation Systems for Stratified Testing

This paper proposes a novel environmental-based method for evaluating the good yield rate (GYR) of systems-on-chip (SoC) during fabrication. Testing and yield evaluation at high confidence are two of the most critical issues for the success of SoC as a viable technology. The proposed method relies on different features of fabrication, which are quantified by the so-called Fabrication environmental parameters (EPs). EPs can be highly correlated to the yield, so they are analyzed using statistical methods to improve its accuracy and ultimately direct the test process to an efficient execution. The novel contributions of the proposed method are: 1) to establish an adequate theoretical foundation for understanding the fabrication process of SoCs together with an assurance of the yield at a high confidence level and 2) to ultimately provide a realistic approach to SoC testing with an accurate yield evaluation. Simulations are provided to demonstrate that the proposed method significantly improves the confidence interval of the estimated yield as compared with existing testing methodologies such as random testing (RT)

Evaluating the Repair of System-on-Chip (SoC) using Connectivity

This paper presents a new model for analyzing the repairability of reconfigurable system-on-chip (RSoC) instrumentation with the repair process. It exploits the connectivity of the interconnected cores in which unreliability factors due to both neighboring cores and the interconnect structure are taken into account. Based on the connectivity, two RSoC repair scheduling strategies, Minimum Number of Interconnections First (I-MIN) and Minimum Number of Neighboring Cores First (C-MIN), are proposed. Two other scheduling strategies, Maximum Number of Interconnections First (I-MAX) and Maximum Number of Neighboring cores First (C-MAX), are also introduced and analyzed to further explore the impact of connectivity-based repair scheduling on the overall repairability of RSoCs. Extensive parametric simulations demonstrate the efficiency of the proposed RSoC repair scheduling strategies; thereby manufacturing ultimately reliable RSoC instrumentation can be achieved

Evolution of Gold Nanoparticles in Radiation Environments

Gold nanoparticles are being explored for several applications in radiation environments, including uses in cancer radiotherapy treatments and advanced satellite or detector applications. In these applications, nanoparticle interactions with energetic neutrons, photons, and charged particles can cause structural damage ranging from single atom displacement events to bulk morphological changes. Due to the diminutive length scales and prodigious surface-to-volume ratios of gold nanoparticles, radiation damage effects are typically dominated by sputtering and surface interactions and can vary drastically from bulk behavior and classical models. Here, we report on contemporary experimental and computational modeling efforts that have contributed to the current understanding of how ionizing radiation environments affect the structure and properties of gold nanoparticles. The future potential for elucidating the active mechanisms in gold nanoparticles exposed to ionizing radiation and the subsequent ability to predictively model the radiation stability and ion beam modification parameters will be discussed

Marshall Space Flight Center Research and Technology Report 2019

Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Centerwe make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This years report features 10 of the Agencys 16 Technology Areas, and I am proud of Marshalls role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Centers capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come

Fault tolerance via diversity for off-the-shelf products: A study with SQL database servers

If an off-the-shelf software product exhibits poor dependability due to design faults, then software fault tolerance is often the only way available to users and system integrators to alleviate the problem. Thanks to low acquisition costs, even using multiple versions of software in a parallel architecture, which is a scheme formerly reserved for few and highly critical applications, may become viable for many applications. We have studied the potential dependability gains from these solutions for off-the-shelf database servers. We based the study on the bug reports available for four off-the-shelf SQL servers plus later releases of two of them. We found that many of these faults cause systematic noncrash failures, which is a category ignored by most studies and standard implementations of fault tolerance for databases. Our observations suggest that diverse redundancy would be effective for tolerating design faults in this category of products. Only in very few cases would demands that triggered a bug in one server cause failures in another one, and there were no coincident failures in more than two of the servers. Use of different releases of the same product would also tolerate a significant fraction of the faults. We report our results and discuss their implications, the architectural options available for exploiting them, and the difficulties that they may present

Sustainable Fault-handling Of Reconfigurable Logic Using Throughput-driven Assessment

A sustainable Evolvable Hardware (EH) system is developed for SRAM-based reconfigurable Field Programmable Gate Arrays (FPGAs) using outlier detection and group testing-based assessment principles. The fault diagnosis methods presented herein leverage throughput-driven, relative fitness assessment to maintain resource viability autonomously. Group testing-based techniques are developed for adaptive input-driven fault isolation in FPGAs, without the need for exhaustive testing or coding-based evaluation. The techniques maintain the device operational, and when possible generate validated outputs throughout the repair process. Adaptive fault isolation methods based on discrepancy-enabled pair-wise comparisons are developed. By observing the discrepancy characteristics of multiple Concurrent Error Detection (CED) configurations, a method for robust detection of faults is developed based on pairwise parallel evaluation using Discrepancy Mirror logic. The results from the analytical FPGA model are demonstrated via a self-healing, self-organizing evolvable hardware system. Reconfigurability of the SRAM-based FPGA is leveraged to identify logic resource faults which are successively excluded by group testing using alternate device configurations. This simplifies the system architect\u27s role to definition of functionality using a high-level Hardware Description Language (HDL) and system-level performance versus availability operating point. System availability, throughput, and mean time to isolate faults are monitored and maintained using an Observer-Controller model. Results are demonstrated using a Data Encryption Standard (DES) core that occupies approximately 305 FPGA slices on a Xilinx Virtex-II Pro FPGA. With a single simulated stuck-at-fault, the system identifies a completely validated replacement configuration within three to five positive tests. The approach demonstrates a readily-implemented yet robust organic hardware application framework featuring a high degree of autonomous self-control

Application of Probabilistic Methods to Turbine Engine Disk Life Prediction and Risk Assessment

Turbine engine disk life prediction and understanding the associated risk remains a significant challenge for today’s designer. Despite advances made in materials testing and characterization, as well as, the application of damage tolerance and linear elastic fracture mechanics modeling, there remains a void in properly assessing loading, geometry, and material design property variability. Add to this the application of advanced hybrid and composite material systems and the need to accurately deal with material variability is even greater. There still remain incidents of failure of critical components which were not properly accounted for by the existing analytical methods, testing, and inspections employed today. Application of probabilistic methods offers an effective and useful approach to modeling this variability while also providing a means by which to assess random variable sensitivity and risk assessment. Current research, as well as, applicable industry and government regulatory guidelines and publications were examined and will be presented. An assessment of the most effective tools, modeling methods, and predictive risk of failure assessments together with recommendations for future work will be discussed. The potential for probabilistic methods to provide a cost-effective way to manage fleet engine and component usage is presented, as well as, its ability to enhance the safe implementation of Retirement for Cause concepts to fleet management

GPU devices for safety-critical systems: a survey

Graphics Processing Unit (GPU) devices and their associated software programming languages and frameworks can deliver the computing performance required to facilitate the development of next-generation high-performance safety-critical systems such as autonomous driving systems. However, the integration of complex, parallel, and computationally demanding software functions with different safety-criticality levels on GPU devices with shared hardware resources contributes to several safety certification challenges. This survey categorizes and provides an overview of research contributions that address GPU devices’ random hardware failures, systematic failures, and independence of execution.This work has been partially supported by the European Research Council with Horizon 2020 (grant agreements No. 772773 and 871465), the Spanish Ministry of Science and Innovation under grant PID2019-107255GB, the HiPEAC Network of Excellence and the Basque Government under grant KK-2019-00035. The Spanish Ministry of Economy and Competitiveness has also partially supported Leonidas Kosmidis with a Juan de la Cierva Incorporación postdoctoral fellowship (FJCI-2020- 045931-I).Peer ReviewedPostprint (author's final draft

COMPUTATIONAL STUDIES OF STRUCTURE–FUNCTION RELATIONSHIPS OF SUPPORTED AND UNSUPPORTED METAL NANOCLUSTERS

Fuel cells have been demonstrated to be promising power generation devices to address the current global energy and environmental challenges. One of the many barriers to commercialization is the cost of precious catalysts needed to achieve sufficient power output. Platinum-based materials play an important role as electrocatalysts in energy conversion technologies. In order to improve catalytic efficiency and facilitate rational design and development of new catalysts, structure–function relationships that underpin catalytic activity must be understood at a fundamental level. First, we present a systematic analysis of CO adsorption on Pt nanoclusters in the 0.2-1.5 nm size range with the aim of unraveling size-dependent trends and developing predictive models for site-specific adsorption behavior. Using an empirical-potential-based Genetic Algorithm (GA) and DFT modeling, we show that there exists a size window (40–70 atoms) over which Pt nanoclusters bind CO weakly, the binding energies being comparable to those on (111) or (100) facets. The size-dependent adsorption energy trends are, however, distinctly non-monotonic and are not readily captured using traditional descriptors such as d-band energies or (generalized) coordination numbers of the Pt binding sites. Instead, by applying machine-learning algorithms, we show that multiple descriptors, broadly categorized as structural and electronic descriptors, are essential for qualitatively capturing the CO adsorption trends. Nevertheless, attaining quantitative accuracy requires further refinement and we propose the use of an additional descriptor – the fully-frozen adsorption energy – that is a computationally inexpensive probe of CO–Pt bond formation. With these three categories of descriptors, we achieve an absolute mean error in CO adsorption energy prediction of 0.12 eV, which is similar to the underlying error of DFT adsorption calculations. Our approach allows for building quantitatively predictive models of site-specific adsorbate binding on realistic, low-symmetry nanostructures, which is an important step in modeling reaction networks as well as for rational catalyst design in general. Thereafter, to understand support effects on the activity of Pt nanoclusters, we employ a combination of empirical potential simulations and DFT calculations to investigate structure–function relationships of small PtN (N = 2-80) clusters on model carbon (graphene) supports. A bond-order empirical potential is employed within a GA to go beyond local optimizations in obtaining minimum-energy structures of PtN clusters on pristine as well as defective graphene supports. Point defects in graphene strongly anchor Pt clusters and also appreciably affect the morphologies of small clusters, which are characterized via various structural metrics such as the radius of gyration, average bond length, and average coordination number. A key finding from the structural analysis is that the fraction of potentially active surface sites in supported clusters is maximized for stable Pt clusters in the size range of 20-30 atoms, which provides a useful design criterion for optimal utilization of the precious metal. Through selected ab initio studies, we find a consistent trend for charge transfer from small Pt clusters to defective graphene supports resulting in the lowering of the cluster d-band center, which has implications for the overall activity and poisoning of the catalyst. The combination of a robust empirical potential-based GA for structural optimization with ab initio calculations opens up avenues for systematic studies of supported catalyst clusters at much larger system sizes than are accessible to purely ab initio approaches. Finally, we present a self-consistent charge density-functional tight-binding (SCC-DFTB) parameterization for PtRu alloys, which is developed by employing a training set of alloy cluster energies and forces obtained from Kohn-Sham DFT calculations. Extensive simulations of a testing set of PtRu alloy nanoclusters show that this SCC-DFTB scheme is capable of capturing cluster formation energies with high accuracy relative to DFT calculations. The new SCC-DFTB parameterization is employed within a GA to search for global minima of PtRu clusters in the range of 13-81 atoms and the emergence of Ru-core/Pt-shell structures at intermediate alloy compositions is systematically demonstrated. Our new SCC-DFTB parameterization enables computationally inexpensive modeling and exploration of structure–function relationships for Pt-Ru clusters that are among the best-performing catalysts in numerous energy applications