Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457827
Kim Hintz, M. Dazer
This paper introduces a novel simulation that combines the reliability modeling of photovoltaic (PV) system components with their respective I-V characteristic curves. The simulation encompasses varied system designs, including string inverter and module-integrated inverter concepts, and accounts for diverse solar irradiation and shading scenarios across multiple operating conditions. By integrating reliability aspects with real-world operating scenarios, the approach offers a holistic view of PV system performance considering the impact of frequency and occurrence of component failures and different repair strategies. The research delves deep into how the inverter reliability and the choice of repair strategies can influence the profitability of a PV system in various environmental conditions. A thorough statistical analysis revealed that both inverter lifetime and repair strategy have a significant effect on profitability. Key results indicate that in Germany, the optimal repair limit for the string inverter design is approximately at a 2.5 kWh daily power loss. In contrast, if the inverter lifetime in the module-inverter concept is sufficiently long, no repairs are necessary to achieve an optimal profit. These findings highlight the intricate relationship between inverter lifetime, application scenarios, and environmental conditions when determining optimal repair strategies.
{"title":"Reliability and Availability Analysis of Photovoltaic Systems","authors":"Kim Hintz, M. Dazer","doi":"10.1109/RAMS51492.2024.10457827","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457827","url":null,"abstract":"This paper introduces a novel simulation that combines the reliability modeling of photovoltaic (PV) system components with their respective I-V characteristic curves. The simulation encompasses varied system designs, including string inverter and module-integrated inverter concepts, and accounts for diverse solar irradiation and shading scenarios across multiple operating conditions. By integrating reliability aspects with real-world operating scenarios, the approach offers a holistic view of PV system performance considering the impact of frequency and occurrence of component failures and different repair strategies. The research delves deep into how the inverter reliability and the choice of repair strategies can influence the profitability of a PV system in various environmental conditions. A thorough statistical analysis revealed that both inverter lifetime and repair strategy have a significant effect on profitability. Key results indicate that in Germany, the optimal repair limit for the string inverter design is approximately at a 2.5 kWh daily power loss. In contrast, if the inverter lifetime in the module-inverter concept is sufficiently long, no repairs are necessary to achieve an optimal profit. These findings highlight the intricate relationship between inverter lifetime, application scenarios, and environmental conditions when determining optimal repair strategies.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"294 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457810
Navid Zaman, You-Jung Jun, Daniel Chan
Failure detection and isolation (FDI) is a crucial step in diagnostics and is quickly shifting to towards using analytical techniques such as machine learning and deep learning, rather than traditional rules-based approaches. This is partially due to the availability of sensor systems, hardware and networking allowing for a vast collection and processing of data. However, this information is prone to issues such as noise, corruption, poor formatting and recording practices. In most cases, a diagnostics project may stall midway due to late discovery of these problems. This paper proposes exploring the data beforehand, to locate issues in the data and/or optimize data quality to maximize performance or explain possible performance loss. Various techniques such as data visualization, statistical analysis and feature importance are mentioned. Most importantly, a domain knowledge set is to be integrated with such correlation-based methods to ensure that data quality decisions are made with understanding of the system. The limitations of such analysis including scalability and interpretation issues are discussed as well, leading to proposals of possible future paths to improvement such as sensor fusion and AI-based recommendations.
{"title":"Exploratory Data Analysis for Failure Detection and Isolation in Complex Systems","authors":"Navid Zaman, You-Jung Jun, Daniel Chan","doi":"10.1109/RAMS51492.2024.10457810","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457810","url":null,"abstract":"Failure detection and isolation (FDI) is a crucial step in diagnostics and is quickly shifting to towards using analytical techniques such as machine learning and deep learning, rather than traditional rules-based approaches. This is partially due to the availability of sensor systems, hardware and networking allowing for a vast collection and processing of data. However, this information is prone to issues such as noise, corruption, poor formatting and recording practices. In most cases, a diagnostics project may stall midway due to late discovery of these problems. This paper proposes exploring the data beforehand, to locate issues in the data and/or optimize data quality to maximize performance or explain possible performance loss. Various techniques such as data visualization, statistical analysis and feature importance are mentioned. Most importantly, a domain knowledge set is to be integrated with such correlation-based methods to ensure that data quality decisions are made with understanding of the system. The limitations of such analysis including scalability and interpretation issues are discussed as well, leading to proposals of possible future paths to improvement such as sensor fusion and AI-based recommendations.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"282 11","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457654
Bernhard Kaiser, Michael Soden, Nils Heuermann
This paper presents a model-based systems engineering (MBSE) workflow in compliance with aerospace safety standards, based on the new SysML v2 language and with particular focus on executable models. We extend the SysML modeling concepts by Component Fault Trees (CFTs) to form an integrated modular Model-Based Safety Analysis (MBSA) approach. We showcase our approach by an Unmanned Aerial Vehicle (search&rescue drone) and discuss how this approach can speed up the process and increase the consistency, modularity, and reuse of the design and safety analysis of the system.
{"title":"A UAV Case Study on an MBSE Workflow with Integrated Modular Safety and Reliability Analysis","authors":"Bernhard Kaiser, Michael Soden, Nils Heuermann","doi":"10.1109/RAMS51492.2024.10457654","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457654","url":null,"abstract":"This paper presents a model-based systems engineering (MBSE) workflow in compliance with aerospace safety standards, based on the new SysML v2 language and with particular focus on executable models. We extend the SysML modeling concepts by Component Fault Trees (CFTs) to form an integrated modular Model-Based Safety Analysis (MBSA) approach. We showcase our approach by an Unmanned Aerial Vehicle (search&rescue drone) and discuss how this approach can speed up the process and increase the consistency, modularity, and reuse of the design and safety analysis of the system.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"276 12","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457743
Benjamin Werner, Benjamin Schumeg, Jason E. Summers, V. Berisha
To address the technical challenges associated with the verification, validation, assurance, and trust of Artificial Intelligence and Machine Learning (AI/ML) in safety critical applications, ARiA in partnership with Arizona State University (ASU) proposed the framework of a Machine Learning Qualification Process (MLQP) in response to a Small Business Technology Transfer (STTR) solicitation. The MLQP incorporates measures and metrics to qualify data sets and models and considerations for the use of data cards, feature cards, and model cards. The US Army Combat Capabilities Development Command Armaments Center (DEVCOM AC) has been developing a roadmap [1] to mitigate the risks associated with the development and deployment of AI/ML enabled systems. The proposed MLQP addresses many of the key challenges and considerations from that roadmap to enable the development of assured and trusted AI/ML enabled systems. This paper will examine how the proposed approach can be leveraged as a tool to build assurance into the cycle of AI/ML development and deployment to ensure safe and reliable systems and the alignment to Army assurance practices as well as DoD guidance.
{"title":"Machine Learning Qualification Process and Impact to System Assurance","authors":"Benjamin Werner, Benjamin Schumeg, Jason E. Summers, V. Berisha","doi":"10.1109/RAMS51492.2024.10457743","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457743","url":null,"abstract":"To address the technical challenges associated with the verification, validation, assurance, and trust of Artificial Intelligence and Machine Learning (AI/ML) in safety critical applications, ARiA in partnership with Arizona State University (ASU) proposed the framework of a Machine Learning Qualification Process (MLQP) in response to a Small Business Technology Transfer (STTR) solicitation. The MLQP incorporates measures and metrics to qualify data sets and models and considerations for the use of data cards, feature cards, and model cards. The US Army Combat Capabilities Development Command Armaments Center (DEVCOM AC) has been developing a roadmap [1] to mitigate the risks associated with the development and deployment of AI/ML enabled systems. The proposed MLQP addresses many of the key challenges and considerations from that roadmap to enable the development of assured and trusted AI/ML enabled systems. This paper will examine how the proposed approach can be leveraged as a tool to build assurance into the cycle of AI/ML development and deployment to ensure safe and reliable systems and the alignment to Army assurance practices as well as DoD guidance.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"283 4","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457819
Rong Pan, Guanqi Fang
Degradation tests are often accompanied by pre-defined product inspection plans because real-time monitoring of product performance is difficult, if not impossible, in most cases. If the purpose of an inspection plan is to predict a failure time before an actual failure happens, it is reasonable to increase inspection frequency gradually over time as the test unit deteriorates over time. Curiously, this strategy has not been discussed much in the degradation test (DT) planning literature. In this paper, we propose a risk-based inspection (RBI) method to determine the final inspection time. By balancing the probability of failure and the consequence of failure, we place the optimal inspection time at the moment of highest failure risk, thus it is closer to the soft failure time that engineers actually care about. Furthermore, we test three inspection scheduling strategies - the equal-distance inspection time strategy, the equal-proportion inspection time strategy, and the middle inspection time strategy. It is found that progressively adding middle inspection times can enhance the prediction property of the degradation model. Through simulation, we show that this inspection plan performs better than other plans.
{"title":"Planning Inspection Times for Degradation Tests","authors":"Rong Pan, Guanqi Fang","doi":"10.1109/RAMS51492.2024.10457819","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457819","url":null,"abstract":"Degradation tests are often accompanied by pre-defined product inspection plans because real-time monitoring of product performance is difficult, if not impossible, in most cases. If the purpose of an inspection plan is to predict a failure time before an actual failure happens, it is reasonable to increase inspection frequency gradually over time as the test unit deteriorates over time. Curiously, this strategy has not been discussed much in the degradation test (DT) planning literature. In this paper, we propose a risk-based inspection (RBI) method to determine the final inspection time. By balancing the probability of failure and the consequence of failure, we place the optimal inspection time at the moment of highest failure risk, thus it is closer to the soft failure time that engineers actually care about. Furthermore, we test three inspection scheduling strategies - the equal-distance inspection time strategy, the equal-proportion inspection time strategy, and the middle inspection time strategy. It is found that progressively adding middle inspection times can enhance the prediction property of the degradation model. Through simulation, we show that this inspection plan performs better than other plans.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"250 10","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457826
Myron Hecht, Scott Agena
This paper demonstrates the use of Model Based Systems Engineering and SysML for a Kubernetes cluster and how the results of such models can be used for design and architectural decisions. There are two major innovations in this paper. The first is use of SysML for reliabiltiy/availability modeling of a computer system with a containerized software architecture using Kubenetes, the most common orchestration (system management) platform for containerized architectures [1]. By using SysML, it is possible to incorporate reliability and availability models into a Model Based Systems Engineering (MBSE) development process. The benefits are better design decisions and lower cost. The second innovation is a description of how the Litmus Chaos failure simulation testing framework [8] can be used for empirical measurement of reliability/availability model parameters. Systems built on the Kubernetes platform can be developed incrementally so that executable systems with partial functionality can be observed and measured to provide early feedback on system reliability and availability performance. The early feedback enables more accurate assessments and more effective corrective actions if necessary. The numerical results of the SysML model were verified using an independent model. The results of the two models agreed to the 9th significant figure or better.
{"title":"A Reliability and Availability Model of a Kubernetes Cluster Using SysML","authors":"Myron Hecht, Scott Agena","doi":"10.1109/RAMS51492.2024.10457826","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457826","url":null,"abstract":"This paper demonstrates the use of Model Based Systems Engineering and SysML for a Kubernetes cluster and how the results of such models can be used for design and architectural decisions. There are two major innovations in this paper. The first is use of SysML for reliabiltiy/availability modeling of a computer system with a containerized software architecture using Kubenetes, the most common orchestration (system management) platform for containerized architectures [1]. By using SysML, it is possible to incorporate reliability and availability models into a Model Based Systems Engineering (MBSE) development process. The benefits are better design decisions and lower cost. The second innovation is a description of how the Litmus Chaos failure simulation testing framework [8] can be used for empirical measurement of reliability/availability model parameters. Systems built on the Kubernetes platform can be developed incrementally so that executable systems with partial functionality can be observed and measured to provide early feedback on system reliability and availability performance. The early feedback enables more accurate assessments and more effective corrective actions if necessary. The numerical results of the SysML model were verified using an independent model. The results of the two models agreed to the 9th significant figure or better.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"294 4","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457600
Abhishek Paul, Roderick Y. Son, Shiv A. Balodi, K. Crooks
Machine learning applications have seen an exponential rise in prevalence across many different industries including healthcare, banking, manufacturing, and defense. While there is a lot of potential for machine learning applications, successful development and productionization is not assured. To prevent failures and ensure success, a Machine Learning Operations (MLOps) Failure Modes and Effects Analysis (FMEA) is proposed as a proactive structured approach for risk identification and mitigation.
{"title":"MLOps FMEA: A Proactive & Structured Approach to Mitigate Failures and Ensure Success for Machine Learning Operations","authors":"Abhishek Paul, Roderick Y. Son, Shiv A. Balodi, K. Crooks","doi":"10.1109/RAMS51492.2024.10457600","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457600","url":null,"abstract":"Machine learning applications have seen an exponential rise in prevalence across many different industries including healthcare, banking, manufacturing, and defense. While there is a lot of potential for machine learning applications, successful development and productionization is not assured. To prevent failures and ensure success, a Machine Learning Operations (MLOps) Failure Modes and Effects Analysis (FMEA) is proposed as a proactive structured approach for risk identification and mitigation.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"2 9","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457828
Michael L. Potter, Miru D. Jun
We implement a Bayesian inference process for Neural Networks to model the time to failure of highly reliable weapon systems with interval-censored data and time-varying covariates. We analyze and benchmark our approach, LaplaceNN, on synthetic and real datasets with standard classification metrics such as Receiver Operating Characteristic (ROC) Area Under Curve (AUC) Precision-Recall (PR) AUC, and reliability curve visualizations.
{"title":"Do Bayesian Neural Networks Weapon System Improve Predictive Maintenance?","authors":"Michael L. Potter, Miru D. Jun","doi":"10.1109/RAMS51492.2024.10457828","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457828","url":null,"abstract":"We implement a Bayesian inference process for Neural Networks to model the time to failure of highly reliable weapon systems with interval-censored data and time-varying covariates. We analyze and benchmark our approach, LaplaceNN, on synthetic and real datasets with standard classification metrics such as Receiver Operating Characteristic (ROC) Area Under Curve (AUC) Precision-Recall (PR) AUC, and reliability curve visualizations.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"18 5","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457615
Benjamin Werner, Benjamin Schumeg, Jon Vigil, Shane N. Hall, Benjamin G. Thengvall, Mikel D. Petty
The US Army solicited partners through a Broad Agency Announcement to propose solutions under a Small Business Technology Transfer contract mechanism for the program “Metrics and Methods for Verification, Validation, Assurance and Trust of Machine Learning Models & Data for Safety-Critical Applications in Armaments Systems.” OptTek Systems, Inc. and University of Alabama in Huntsville (UAH) were one of the selected proposals for Phase I. Under this contract agreement OptTek and UAH set the goal to research & develop (R&D) fundamental metrics & measures for the certification & qualification of ML training data sets & models. Of particular note, the use of a safety score calculated from the accuracy as well as a dedicated look at data quality have been demonstrated as reasonable approaches to the proposed topic. As the Technical Point of Contact for this effort, the US Army Combat Capabilities Development Command Armaments Center (DEVCOM AC) authored the topic and provided guidance on the effort to align with mission objectives. This paper is an exploration of the research and development conducted by OptTek and UAH within the framework of how it may be applied to the assurance of systems to be developed by the US Army and augment practices in reliability and safety.
美国陆军通过 "广泛机构公告"（Broad Agency Announcement）征集合作伙伴，在小企业技术转让合同机制下为 "用于军备系统安全关键应用的机器学习模型和数据的验证、确认、保证和信任的度量标准和方法 "项目提出解决方案。根据该合同协议，OptTek Systems 公司和阿拉巴马大学亨茨维尔分校（UAH）的目标是研究和开发（R&D）用于 ML 训练数据集和模型认证和鉴定的基本指标和措施。特别值得注意的是，根据准确性计算的安全分数以及对数据质量的专门研究已被证明是解决拟议主题的合理方法。作为这项工作的技术联络点，美国陆军作战能力发展司令部军备中心（DEVCOM AC）撰写了这一课题，并提供了与任务目标相一致的工作指导。本文探讨了 OptTek 和 UAH 在如何将其应用于美国陆军即将开发的系统保证以及增强可靠性和安全性实践的框架内进行的研究和开发。
{"title":"Measures and Metrics of ML Data and Models to Assure Reliable and Safe Systems","authors":"Benjamin Werner, Benjamin Schumeg, Jon Vigil, Shane N. Hall, Benjamin G. Thengvall, Mikel D. Petty","doi":"10.1109/RAMS51492.2024.10457615","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457615","url":null,"abstract":"The US Army solicited partners through a Broad Agency Announcement to propose solutions under a Small Business Technology Transfer contract mechanism for the program “Metrics and Methods for Verification, Validation, Assurance and Trust of Machine Learning Models & Data for Safety-Critical Applications in Armaments Systems.” OptTek Systems, Inc. and University of Alabama in Huntsville (UAH) were one of the selected proposals for Phase I. Under this contract agreement OptTek and UAH set the goal to research & develop (R&D) fundamental metrics & measures for the certification & qualification of ML training data sets & models. Of particular note, the use of a safety score calculated from the accuracy as well as a dedicated look at data quality have been demonstrated as reasonable approaches to the proposed topic. As the Technical Point of Contact for this effort, the US Army Combat Capabilities Development Command Armaments Center (DEVCOM AC) authored the topic and provided guidance on the effort to align with mission objectives. This paper is an exploration of the research and development conducted by OptTek and UAH within the framework of how it may be applied to the assurance of systems to be developed by the US Army and augment practices in reliability and safety.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"15 9","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457769
O. L. Kedienhon
The design and manufacture of Gallium Nitride Monolithic Microwave Integrated Circuits (GaN MMICs) have matured in recent years, yet there is no existing reliability prediction modeling method, like the widely utilized one for Gallium Arsenide (GaAs) MMIC in MIL-HDBK-217. This paper develops and presents a new method for these devices by modifying key parameters in the existing GaAs MMIC standard model to incorporate the improved temperature properties of GaN, and typical activation energy, Ea obtained from accelerated life tests. The resulting model will be an invaluable tool for the reliability modeling of GaN MMICs for device suppliers and customers across all industries.
{"title":"Scaled Reliability Prediction Model for Gallium Nitride (GaN) Monolithic Microwave Integrated Circuit","authors":"O. L. Kedienhon","doi":"10.1109/RAMS51492.2024.10457769","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457769","url":null,"abstract":"The design and manufacture of Gallium Nitride Monolithic Microwave Integrated Circuits (GaN MMICs) have matured in recent years, yet there is no existing reliability prediction modeling method, like the widely utilized one for Gallium Arsenide (GaAs) MMIC in MIL-HDBK-217. This paper develops and presents a new method for these devices by modifying key parameters in the existing GaAs MMIC standard model to incorporate the improved temperature properties of GaN, and typical activation energy, Ea obtained from accelerated life tests. The resulting model will be an invaluable tool for the reliability modeling of GaN MMICs for device suppliers and customers across all industries.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"264 3","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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