2021 IEEE International Test Conference (ITC)最新文献_第3页

2021 IEEE International Test Conference (ITC) Pub Date : 2021-10-01 DOI: 10.1109/ITC50571.2021.00024

Shahram Rasoolzadeh, Aein Rezaei Shahmirzadi, A. Moradi

引用次数: 2

Triplet Convolutional Networks for Classifying Mixed-Type WBM Patterns with Noisy Labels 带噪声标签的混合型WBM模式的三重卷积网络分类

2021 IEEE International Test Conference (ITC) Pub Date : 2021-10-01 DOI: 10.1109/ITC50571.2021.00028

Chenwei Liu, Qiaoyue Tang

{"title":"Triplet Convolutional Networks for Classifying Mixed-Type WBM Patterns with Noisy Labels","authors":"Chenwei Liu, Qiaoyue Tang","doi":"10.1109/ITC50571.2021.00028","DOIUrl":"https://doi.org/10.1109/ITC50571.2021.00028","url":null,"abstract":"Wafer Bin Maps (WBM) frequently show various spatial failure patterns that provide crucial information for engineers to identify the root cause of failures and their consequent low yield. To shorten the root-cause diagnosis process, it is important to classify different failure patterns with high accuracy, especially when there is mixed type of failure patterns on the same wafer. The main challenges of mixed type classification in WBMs include: 1) Lack of accurately annotated real-world training dataset, 2) Imbalanced/long-tail distributions among classes, 3) Synthesized training data usually cannot reflect the practical application conditions. In this paper, we propose a weakly supervised learning approach and use an ensemble method based on triplet CNN models to classify mixed-type failure patterns in WBMs. We train the models based on the public WM-811K dataset, which is collected from real products but with only single-label annotations. We demonstrate that such models could mitigate the imbalanced class distribution and being able to learn efficiently from a weakly labeled dataset and achieve superior performances on the classification of real wafer maps with long-tail distributed mixed type failures. We also discuss the practical considerations of implementing such models and the advantages of using triplet over binary CNN models.","PeriodicalId":147006,"journal":{"name":"2021 IEEE International Test Conference (ITC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123173072","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

Brain-Inspired Computing for Wafer Map Defect Pattern Classification 基于大脑的晶圆图缺陷模式分类计算

2021 IEEE International Test Conference (ITC) Pub Date : 2021-10-01 DOI: 10.1109/ITC50571.2021.00020

P. Genssler, H. Amrouch

引用次数: 23

Accessing general IEEE Std. 1687 networks via functional ports 通过功能端口访问通用IEEE标准1687网络

2021 IEEE International Test Conference (ITC) Pub Date : 2021-10-01 DOI: 10.1109/ITC50571.2021.00051

E. Larsson, P. Murali, Ziling Zhang

引用次数: 1

Low Power Shift and Capture through ATPG-Configured Embedded Enable Capture Bits 低功耗转换和捕获通过atpg配置嵌入式使能捕获位

2021 IEEE International Test Conference (ITC) Pub Date : 2021-10-01 DOI: 10.1109/ITC50571.2021.00045

Yi Sun, Hui Jiang, L. Ramakrishnan, Jennifer Dworak, Kundan Nepal, T. Manikas, R. I. Bahar

引用次数: 1

Testability-Enhancing Resynthesis of Reconfigurable Scan Networks 可重构扫描网络的可测试性增强重构

2021 IEEE International Test Conference (ITC) Pub Date : 2021-10-01 DOI: 10.1109/ITC50571.2021.00009

N. Lylina, Chih-Hao Wang, H. Wunderlich

引用次数: 4

Open-short Normalization Method for a Quick Defect Identification in Branched Traces with High-resolution Time-domain Reflectometry 基于高分辨率时域反射法的分支轨迹缺陷快速识别开短归一化方法

2021 IEEE International Test Conference (ITC) Pub Date : 2021-10-01 DOI: 10.1109/ITC50571.2021.00032

Y. Shang, M. Shinohara, Eiji Kato, M. Hashimoto, J. Kiljan

引用次数: 1

Revisit to Accurate ADC Testing with Incoherent Sampling Using Proper Sinusoidal Signal and Sampling Frequencies 用适当的正弦信号和采样频率进行非相干采样的精确ADC测试

2021 IEEE International Test Conference (ITC) Pub Date : 2021-10-01 DOI: 10.1109/ITC50571.2021.00038

Keno Sato, Takashi Ishida, Toshiyuki Okamoto, Tamotsu Ichikawa, Jianglin Wei, Takayuki Nakatani, Yujie Zhao, Shogo Katayama, Shuhei Yamamoto, A. Kuwana, K. Hatayama, Haruo Kobayashi

{"title":"Revisit to Accurate ADC Testing with Incoherent Sampling Using Proper Sinusoidal Signal and Sampling Frequencies","authors":"Keno Sato, Takashi Ishida, Toshiyuki Okamoto, Tamotsu Ichikawa, Jianglin Wei, Takayuki Nakatani, Yujie Zhao, Shogo Katayama, Shuhei Yamamoto, A. Kuwana, K. Hatayama, Haruo Kobayashi","doi":"10.1109/ITC50571.2021.00038","DOIUrl":"https://doi.org/10.1109/ITC50571.2021.00038","url":null,"abstract":"This paper describes that the mature ADC testing method with a simple test system using the incoherent sampling and the standard algorithm of windowing and FFT with 4Kpoint data can measure the SINAD of our target 12-bit SAR ADC accurately by proper setting of the input and sampling frequencies, which is industry-friendly. We show the input sinusoidal signal and sampling clock frequency relationship for accurate testing of the ADC dynamic characteristics with an incoherent sampling method using a flat-top window. We have clarified the measured SINAD accuracy of the input signal frequency dependency for a fixed sampling frequency, a specified resolution of the ADC under test and a given number of FFT points (data samples) in the incoherent sampling environment. Mature technology combinations with their optimal usage and without advanced methods can lead to the low-cost high-quality testing of the ADC, which can be well accepted in industry. Their analysis, simulation and experimental results are shown.","PeriodicalId":147006,"journal":{"name":"2021 IEEE International Test Conference (ITC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131497915","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

Hierarchical Failure Modeling and Machine Learning Assisted Correction of Electro-Mechanical Subsystem Failures in Autonomous Vehicles 自动驾驶汽车机电子系统故障分层建模与机器学习辅助校正

2021 IEEE International Test Conference (ITC) Pub Date : 2021-10-01 DOI: 10.1109/ITC50571.2021.00055

C. Amarnath, Md Imran Momtaz, A. Chatterjee

引用次数: 0

The Security Enhancement Techniques of the Double-layer PUF Against the ANN-based Modeling Attack 针对基于ann的建模攻击的双层PUF安全增强技术

2021 IEEE International Test Conference (ITC) Pub Date : 2021-10-01 DOI: 10.1109/ITC50571.2021.00014

Yongliang Chen, Xiaole Cui, Wenqiang Ye, Xiaohui Cui

{"title":"The Security Enhancement Techniques of the Double-layer PUF Against the ANN-based Modeling Attack","authors":"Yongliang Chen, Xiaole Cui, Wenqiang Ye, Xiaohui Cui","doi":"10.1109/ITC50571.2021.00014","DOIUrl":"https://doi.org/10.1109/ITC50571.2021.00014","url":null,"abstract":"The physical unclonable function (PUF) against the modeling attack is of great concern in recent years, since the modeling attack has been proved to be a serious security threat to the PUF circuits. The double-layer PUF was reported as a PUF scheme to resist the fully connected artificial neural network based modeling attack, and its test chip was fabricated and tested. This work proposes an artificial neural network (ANN) based modeling method according to the working principle of the target PUF, and successfully attacks the double-layer PUF. To enhance the anti-modeling-attack capability of the double-layer PUF, the address swapping, the XORing, and the dimensional extension techniques are proposed. The attack results show that the prediction accuracy of the proposed ANN-based model with the proposed techniques drops obviously. And the prediction accuracy is about 50.04% if all the three proposed techniques are applied in combination. It manifests that the proposed security enhancement techniques are able to improve the resilience of the double-layer PUF against the modeling attacks effectively. Both the randomness and uniqueness of the improved doublelayer PUFs are approximate to the ideal value (50%), and the reliability of the improved PUFs remain unchanged compared with the original counterpart because the operations on the resistive random memory (RRAM) array are the same.","PeriodicalId":147006,"journal":{"name":"2021 IEEE International Test Conference (ITC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121883293","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