2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)最新文献_第3页

GaAs RF Amplifier Field Failure Analysis and Reliability Prediction in 5G AAU System 5G AAU系统中GaAs射频放大器场失效分析及可靠性预测

2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) Pub Date : 2022-07-18 DOI: 10.1109/IPFA55383.2022.9915740

Lin Shi, Chong Wang, Xiaolong Cai, Zhengya Cao, Xiao-hua Ma, Xiangyang Duan

{"title":"GaAs RF Amplifier Field Failure Analysis and Reliability Prediction in 5G AAU System","authors":"Lin Shi, Chong Wang, Xiaolong Cai, Zhengya Cao, Xiao-hua Ma, Xiangyang Duan","doi":"10.1109/IPFA55383.2022.9915740","DOIUrl":"https://doi.org/10.1109/IPFA55383.2022.9915740","url":null,"abstract":"The paper studies the field failure of a radio frequency differential amplifier in the 5G Active Antenna Unit base station; gallium arsenide(GaAs) die substrate crack was found through failure analysis on the returned units. Packaging process investigation found ejector marks on the blue film were abnormal when executing die bonding. Some amplifiers with slight cracks have passed the functional test, and base station manufacturers’ production test has not effectively intercepted the defect units. Eventually, defect units outflow and fail during field operation. Through the statistics of failure time and reliability data analysis, the results show that the failure is a typical log-normal distribution with a correlation coefficient of 0.87, and the failure rate decreases with time, indicating that the case belongs to an early failure, which once again proves the theory that the early failure is the outflow of defective products. It is estimated that the cumulative failure ratio in 1 year is 0.58% which was confirmed by actual field performance. This study can be a reference for die crack failure analysis and its reliability risk prediction.","PeriodicalId":378702,"journal":{"name":"2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128715244","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}

引用次数: 1

AI Detection of Body Defects and Corrosion on Leads in Electronic Components, and a study of their Occurrence 电子元件本体缺陷和引线腐蚀的人工智能检测及其发生机理研究

2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) Pub Date : 2022-07-18 DOI: 10.1109/IPFA55383.2022.9915776

Eyal Weiss

{"title":"AI Detection of Body Defects and Corrosion on Leads in Electronic Components, and a study of their Occurrence","authors":"Eyal Weiss","doi":"10.1109/IPFA55383.2022.9915776","DOIUrl":"https://doi.org/10.1109/IPFA55383.2022.9915776","url":null,"abstract":"A large-scale evaluation of the quality of electronic components at the time of the electronic board assembly is presented. Counterfeit components are often recycled or old component, therefore, the quality of components and the soldering leads is a good indicator of the component’s authenticity. The quality of the components is evaluated based on their visual appearance by quantifying their visual defects and the corrosion evidence as they appear on the component and its soldering leads. The effect of body defects and corrosion in the soldering leads on the reliability of the component bond to the board is reviewed. A machine learning method to detect body defects and evidence of corrosion on soldering leads is presented. Over 11 million components images were inspected by the presented AI algorithm. It is shown that 290 components out of a million had body visual defects that cannot be seen by conventional AOI. In addition, over 1,100 out of million had visible corrosion evidence on their soldering leads. Corrosion on the soldering not only affects the production yield but is the most common cause for random statistical failures in the field resulting in products failure. The presented method allows inspection of all the components used in production thus reducing the risk of failures in the field caused by poor quality electronic components originating from counterfeit, and poor storage or handling conditions.","PeriodicalId":378702,"journal":{"name":"2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128746547","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}

引用次数: 2

Automated Defect Classification In Semiconductor Devices Using Deep Learning Networks 基于深度学习网络的半导体器件缺陷自动分类

2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) Pub Date : 2022-07-18 DOI: 10.1109/IPFA55383.2022.9915706

A. S. Nair, P. Hoffrogge, P. Czurratis, E. Kuehnicke, Mario Wolf

{"title":"Automated Defect Classification In Semiconductor Devices Using Deep Learning Networks","authors":"A. S. Nair, P. Hoffrogge, P. Czurratis, E. Kuehnicke, Mario Wolf","doi":"10.1109/IPFA55383.2022.9915706","DOIUrl":"https://doi.org/10.1109/IPFA55383.2022.9915706","url":null,"abstract":"More effective Failure Analysis (FA) technologies are required to meet the upcoming challenges in complex semiconductor devices. Because of recent advances in AI (Artificial Intelligence), we can now concentrate our efforts on developing AI-based algorithms for high precision-automated signal interpretation for failure detection in Scanning Acoustic Microscopes (SAM). Typically, flaw detection in ultrasonic data relies heavily on human expertise, and the majority of automated classifications are based on image-based decision algorithms. For defect classification, the image-based ML approach necessitates a large dataset. On signals, the traditional machine learning approach requires manual feature extraction and selection of the best features. DL approaches are commonly used to automate feature learning and classification from raw signals. This paper proposes a method for creating datasets, preprocessing signals, and semi-supervised signal training for defect classification. For performance evaluation, different DL architectures such as 1D CNN, RNN, and hybrid networks were studied. The models were trained to categorize C4 bumps in flip chips into a defect and intact classes. Even with fewer learnable, 1D-CNN with wavelet applied A-Scan as input outperforms other models with an accuracy of up to 99 percent. The model was then validated by destructive analysis on an unknown sample.","PeriodicalId":378702,"journal":{"name":"2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114350436","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}

引用次数: 2

Detection of Failure Analysis Methods with Image Classification 基于图像分类的故障检测分析方法

2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) Pub Date : 2022-07-18 DOI: 10.1109/IPFA55383.2022.9915737

Selene Lobnig, C. Burmer, Konstantin Schekotihin

引用次数: 0

Use of Energy Filtered TEM to Observe Gate Oxide Breakdown Defects 用能量过滤透射电镜观察栅极氧化物击穿缺陷

2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) Pub Date : 2022-07-18 DOI: 10.1109/IPFA55383.2022.9915750

Ye Chen, Jie Zhu

引用次数: 0

Deep Dive into Systemic Secondary EOS Damage caused by a Process-Related Issue 深入了解由流程相关问题引起的系统二次EOS损坏

2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) Pub Date : 2022-07-18 DOI: 10.1109/IPFA55383.2022.9915725

Saidaliah Sarip, John Carlo Francisco, Tejinder Gandhi, Che-Ping Chen, Jed Paolo Deligente, Jonathan Azares

引用次数: 0

Reduction of FIB induced damage in silicon with Argon sputter clean 用氩气溅射清洁减少硅中FIB引起的损伤

2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) Pub Date : 2022-07-18 DOI: 10.1109/IPFA55383.2022.9915721

K. H. Yip, P. Ang, K. Lee, Y. Yeo, Z. Mo

引用次数: 0

Analysis and Modeling for Reverse Body Bias Stress Impact on HCI Induced Degradation in n-Type EDMOS 反向体偏应力对n型EDMOS HCI诱导降解的影响分析与建模

2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) Pub Date : 2022-07-18 DOI: 10.1109/IPFA55383.2022.9915712

Miao Cai, S. Leang, Kok Wai Chew, P. Tan, A. P. Herlambang, Chunxiang Zhu, Yongxin Guo

引用次数: 1

Failure Analysis for SIP IC after TC reliability test TC可靠性试验后SIP集成电路失效分析

2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) Pub Date : 2022-07-18 DOI: 10.1109/IPFA55383.2022.9915724

Bardon Cui

引用次数: 0

Combining Enhanced Diagnostic-Driven Analysis Scheme and Static Near Infrared Photon Emission Microscopy for Effective Scan Failure Debug 结合增强诊断驱动分析方案和静态近红外光子发射显微镜有效的扫描故障调试

2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) Pub Date : 2022-07-18 DOI: 10.1109/IPFA55383.2022.9915727

S. Moon, D. Nagalingam, Y. Ngow, A. Quah

{"title":"Combining Enhanced Diagnostic-Driven Analysis Scheme and Static Near Infrared Photon Emission Microscopy for Effective Scan Failure Debug","authors":"S. Moon, D. Nagalingam, Y. Ngow, A. Quah","doi":"10.1109/IPFA55383.2022.9915727","DOIUrl":"https://doi.org/10.1109/IPFA55383.2022.9915727","url":null,"abstract":"Software based scan diagnosis is the de facto method for debugging logic scan failures. Physical analysis success rate is high on dies diagnosed with maximum score, one symptom, one suspect and shorter net. This poses a limitation on maximum utilization of scan diagnosis data for PFA. There have been several attempts to combine dynamic fault isolation techniques with scan diagnosis results to enhance the utilization and success rate. However, it is not a feasible approach for foundry due to limited product design and test knowledge and hardware requirements such as probe card and tester. Suitable for a foundry, an enhanced diagnosis-driven analysis scheme was proposed in [1] that classifies the failures as frontend-of-line (FEOL) and backend-of-line (BEOL) improving the die selection process for PFA. In this paper, static NIR PEM and defect prediction approach are applied on dies that are already classified as FEOL and BEOL failures yet considered unsuitable for PFA due to low score, multiple symptoms, and suspects. Successful case studies are highlighted to showcase the effectiveness of using static NIR PEM as the next level screening process to further maximize the scan diagnosis data utilization.","PeriodicalId":378702,"journal":{"name":"2022 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125641997","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}

引用次数: 0