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Detecting Wafer Level Cu Pillar Defects Using Advanced 3D X-ray Microscopy (XRM) with Submicron Resolution 利用先进的亚微米分辨率三维x射线显微镜(XRM)检测晶圆级铜柱缺陷

Proceedings Pub Date : 2023-11-12 DOI: 10.31399/asm.cp.istfa2023p0432

Susan Li, John Frame, Edita Madriaga-Berry, Jose Hulog, Ming Zhang, Masako Terada, Allen Gu, David Taraci

引用次数: 0

Near-Field Synthetic Aperture Focusing Technique to Enhance the Inspection Capability of Multi-Layer HBM Stacks in Scanning Acoustic Microscopy 近场合成孔径聚焦技术提高多层HBM扫描声显微镜检测能力

Proceedings Pub Date : 2023-11-12 DOI: 10.31399/asm.cp.istfa2023p0448

Mario Wolf, Bugra Birki, Peter Hoffrogge, Peter Czurratis, Chaitanya Bakre, Mario Pacheco, Deepak Goyal

引用次数: 0

Nanoprobing for Logical Cell Operational Tests 用于逻辑单元操作测试的纳米探测

Proceedings Pub Date : 2023-11-12 DOI: 10.31399/asm.cp.istfa2023p0420

Branden Long, Yang Su, Yunfei Wang, Christopher Morgan, Md Faisal Kabir, Ramya Padmanaban, Weston Hearne, Tad Daniel

引用次数: 0

Expanding Failure Analysis Using Fluorescence Combined with IR and Raman 红外光谱与拉曼光谱相结合的荧光扩展失效分析

Proceedings Pub Date : 2023-11-12 DOI: 10.31399/asm.cp.istfa2023p0393

Jay Anderson, Michael K. F. Lo, Eoghan P. Dillon, Mustafa Kansiz

引用次数: 0

Failure Localization Technique for Metal and Transistor Defects Through Avalon CAD Navigation and Focused Ion Beam Circuit Edit 基于Avalon CAD导航和聚焦离子束电路编辑的金属和晶体管缺陷故障定位技术

Proceedings Pub Date : 2023-11-12 DOI: 10.31399/asm.cp.istfa2023p0085

Joana Mae De Jesus, Mark Anthony Acedillo

引用次数: 0

Logical to Physical SRAM Bitmap Verification with Fault Localization 逻辑到物理SRAM位图验证与故障定位

Proceedings Pub Date : 2023-11-12 DOI: 10.31399/asm.cp.istfa2023p0160

Alexander Maggiacomo, Rabindra Pahari, Torsten Schaefer, Gregory Billus, Suresh Shekar, Satish Kodali, Felix Beaudoin, Aaron Sinnott

引用次数: 0

Effective Fault Localization Approach for High Speed Transceiver Failure: From Non-destructive to Destructive 高速收发器故障的有效故障定位方法:从无损到破坏性

Proceedings Pub Date : 2023-11-12 DOI: 10.31399/asm.cp.istfa2023p0190

Kuan Lai Khei, Liew Chiun Ning, Goh Lay Lay, Wu Jia Jun, Sailesh Suthar

引用次数: 0

2D and 3D Metrology and Failure Analysis for High Bandwidth Memory Package by Xe and Ar Plasma-FIB 等离子体fib对高带宽存储封装的二维和三维测量及失效分析

Proceedings Pub Date : 2023-11-12 DOI: 10.31399/asm.cp.istfa2023p0370

Melissa Mullen, Mark McClendon, Adam Stokes, Xiaoting Gu, Pete Carleson

{"title":"2D and 3D Metrology and Failure Analysis for High Bandwidth Memory Package by Xe and Ar Plasma-FIB","authors":"Melissa Mullen, Mark McClendon, Adam Stokes, Xiaoting Gu, Pete Carleson","doi":"10.31399/asm.cp.istfa2023p0370","DOIUrl":"https://doi.org/10.31399/asm.cp.istfa2023p0370","url":null,"abstract":"Abstract Continued advancements in the architecture of 3D packaging have increased the challenges in fault isolation and failure analysis (FA), often requiring complex correlative workflows and multiple inference-based methods before targeted root cause analysis (RCA) can be performed. Furthermore, 3D package components such as through-silicon-vias (TSVs) and micro-bumps require sub-surface structural characterization and metrology to aid in process monitoring and development throughout fabrication and integration. Package road-mapping has also called for increased die stacking with decreased pitch, TSV size, and die thickness, and thus requires increased accuracy and precision of various stateof- the-art analytical techniques in the near future. Physical failure analysis (PFA), process monitoring, and process development will therefore depend on reliable, high-resolution data directly measured at the region of interest (ROI) to meet the complexity and scaling challenges. This paper explores the successful application of plasma-FIB (PFIB)/SEM techniques in 2D and 3D regimes and introduces diagonal serial sectioning at package scales as a novel approach for PFA and metrology. Both 2D and 3D analysis will be demonstrated in a high bandwidth memory (HBM) package case-study which can be applied more broadly in 3D packaging.","PeriodicalId":20443,"journal":{"name":"Proceedings","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136353493","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

Advances in High-Resolution Non-Destructive Defect Localization Based on Machine Learning Enhanced Signal Processing 基于机器学习增强信号处理的高分辨率无损缺陷定位研究进展

Proceedings Pub Date : 2023-11-12 DOI: 10.31399/asm.cp.istfa2023p0007

Sebastian Brand, Michael Kögel, Christian Grosse, Frank Altmann, Christian Hollerith, Pascal Gounet

{"title":"Advances in High-Resolution Non-Destructive Defect Localization Based on Machine Learning Enhanced Signal Processing","authors":"Sebastian Brand, Michael Kögel, Christian Grosse, Frank Altmann, Christian Hollerith, Pascal Gounet","doi":"10.31399/asm.cp.istfa2023p0007","DOIUrl":"https://doi.org/10.31399/asm.cp.istfa2023p0007","url":null,"abstract":"Abstract Non-destructive inspection and analysis techniques are crucial for quality assessment and defect analysis in various industries. They enable for screening and monitoring of parts and products without alteration or impact, facilitating the exploration of material interactions and defect formation. With increasing complexity in microelectronic technologies, high reliability, robustness and thus, successful failure analysis is essential. Machine learning (ML) approaches have been developed and evaluated for the analysis of acoustic echo signals and time-resolved thermal responses for assessing their ability for defect detection. In the present paper different ML architectures were evaluated, including 1D and 2D convolutional neural networks (CNNs) after transforming time-domain data into the spectra-land wavelet domains. Results showed that 2D CNN with wavelet domain representation performed best, however at the expense of additional computational effort. Furthermore, ML-based analysis was explored for lock-in thermography to detect and locate defects in the axial dimension based on thermal emissions. While promising, further research is needed to fully realize its potential.","PeriodicalId":20443,"journal":{"name":"Proceedings","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136353498","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

Proposal of Tilt-Axis Adjustment in V-NAND Plan-View without Si Substrate Using Automated Metrology of Transmission Electron Microscope 利用透射电镜自动测量技术实现无Si衬底V-NAND平面视图的倾斜轴调整

Proceedings Pub Date : 2023-11-12 DOI: 10.31399/asm.cp.istfa2023p0279

Dong-yeob Kim, Su-yeon Kim, Woo-jun Kwon, Min-kook Kim, Christopher H. Kang

引用次数: 0