2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS)最新文献

Self-Monitoring, Self-Healing Biomorphic Sensor Technology 自我监测、自我修复生物形态传感器技术

2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2019-10-03 DOI: 10.1109/IOLTS.2019.8854453

A. Richardson, D. Cheneler

引用次数: 2

Fault Modeling and Simulation of Memristor based Gas Sensors 基于忆阻器的气体传感器故障建模与仿真

2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2019-07-01 DOI: 10.1109/IOLTS.2019.8854459

S. Khandelwal, Anu Bala, Vishal Gupta, V. Gupta, M. Ottavi, E. Martinelli, A. Jabir

引用次数: 4

IOLTS 2019 Technical Papers IOLTS 2019技术论文

2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2019-07-01 DOI: 10.1109/iolts.2019.8854408

引用次数: 0

Automated Die Inking through On-line Machine Learning 通过在线机器学习实现自动模印

2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2019-07-01 DOI: 10.1109/IOLTS.2019.8854373

Constantinos Xanthopoulos, Arnold Neckermann, Paulus List, Klaus-Peter Tschernay, Peter Sarson, Y. Makris

引用次数: 2

Towards Improvement of Mission Mode Failure Diagnosis for System-on-Chip 片上系统任务模式故障诊断的改进

2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2019-07-01 DOI: 10.1109/IOLTS.2019.8854388

S. Mhamdi, A. Virazel, P. Girard, A. Bosio, E. Auvray, E. Faehn, A. Ladhar

{"title":"Towards Improvement of Mission Mode Failure Diagnosis for System-on-Chip","authors":"S. Mhamdi, A. Virazel, P. Girard, A. Bosio, E. Auvray, E. Faehn, A. Ladhar","doi":"10.1109/IOLTS.2019.8854388","DOIUrl":"https://doi.org/10.1109/IOLTS.2019.8854388","url":null,"abstract":"In critical (e.g. automotive) applications, Systems-on-Chip (SoC) failures that occurred during mission mode (in the field) are the most critical since they may lead to catastrophic effects. In this context, diagnosis is crucial in order to establish the root cause of observed failures with the best accuracy. With the advent of very deep submicron technologies (i.e. 7 nm), achieving such level of accuracy will become more and more difficult with today’s intra-cell diagnosis tools based on effect-cause or cause-effect paradigms. This will compromise the success of subsequent Physical Failure Analysis (PFA) done on defective SoCs. Machine Learning (ML) is now used in numerous classification problems where the knowledge on some data can be used to classify a new instance of such data. In particular, several ML-based solutions exist to address volume diagnosis for yield improvement. These learning-guided diagnosis approaches start from an existing set of defect candidates and try to minimize this set (eliminate bad candidates) owing to the use of ML tools and numerous data collected during production test (e.g. thousands of failed chips with candidates correctly labeled). Although efficient in volume diagnosis, these approaches cannot be used to identify the root cause of failures in customer returns, since only one failed chip is investigated in this case, with no information about the defective behavior of some other similar chips used in the same conditions (environment, workload, etc.). In this paper, we propose a new learning-guided approach for diagnosis of mission mode failures in customer returns. The proposed approach directly produces a minimum set of good candidates derived from the application of the learning-guided intra-cell diagnosis flow. Results obtained on a set of benchmark circuits, and comparison with a commercial intra-cell diagnosis tool, show the feasibility, effectiveness and accuracy of the proposed approach.","PeriodicalId":383056,"journal":{"name":"2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115519905","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: 3

Can Multi-Layer Microfluidic Design Methods Aid Bio-Intellectual Property Protection? 多层微流体设计方法有助于生物知识产权保护吗?

2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2019-07-01 DOI: 10.1109/IOLTS.2019.8854393

Mohammed Shayan, Sukanta Bhattacharjee, Yong-Ak Song, K. Chakrabarty, R. Karri

引用次数: 3

Variation-aware Fault Modeling and Test Generation for STT-MRAM STT-MRAM的变化感知故障建模与测试生成

2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2019-07-01 DOI: 10.1109/IOLTS.2019.8854376

S. Nair, R. Bishnoi, M. Tahoori, H. Grigoryan, Grigor Tshagharyan

引用次数: 3

Comparison of Radiation Hardness of Stacked Transmission-Gate Flip Flop and Stacked Tristate-Inverter Flip Flop in a 65 nm Thin BOX FDSOI Process 65 nm薄盒FDSOI工艺中堆叠传输门触发器与堆叠三态逆变触发器的辐射硬度比较

2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2019-07-01 DOI: 10.1109/IOLTS.2019.8854436

Mitsunori Ebara, Kodai Yamada, J. Furuta, Kazutoshi Kobayashi

引用次数: 3

Efficient Methodology for ISO26262 Functional Safety Verification ISO26262功能安全验证的有效方法

2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2019-07-01 DOI: 10.1109/IOLTS.2019.8854449

F. A. D. Silva, A. Bagbaba, S. Hamdioui, C. Sauer

引用次数: 3

Modern Hardware Margins: CPUs, GPUs, FPGAs Recent System-Level Studies 现代硬件边际:cpu, gpu, fpga最近的系统级研究

2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS) Pub Date : 2019-07-01 DOI: 10.1109/IOLTS.2019.8854386

D. Gizopoulos, G. Papadimitriou, Athanasios Chatzidimitriou, V. Reddi, Behzad Salami, O. Unsal, A. Cristal, Jingwen Leng

{"title":"Modern Hardware Margins: CPUs, GPUs, FPGAs Recent System-Level Studies","authors":"D. Gizopoulos, G. Papadimitriou, Athanasios Chatzidimitriou, V. Reddi, Behzad Salami, O. Unsal, A. Cristal, Jingwen Leng","doi":"10.1109/IOLTS.2019.8854386","DOIUrl":"https://doi.org/10.1109/IOLTS.2019.8854386","url":null,"abstract":"Modern large-scale computing systems (data centers, supercomputers, cloud and edge setups and high-end cyber-physical systems) employ heterogeneous architectures that consist of multicore CPUs, general-purpose many-core GPUs, and programmable FPGAs. The effective utilization of these architectures poses several challenges, among which a primary one is power consumption. Voltage reduction is one of the most efficient methods to reduce power consumption of a chip. With the galloping adoption of hardware accelerators (i.e., GPUs and FPGAs) in large datacenters and other large-scale computing infrastructures, a comprehensive evaluation of the safe voltage reduction levels for each different chip can be employed for efficient reduction of the total power. We present a survey of recent studies in voltage margins reduction at the system level for modern CPUs, GPUs and FPGAs. The pessimistic voltage guardbands inserted by the silicon vendors can be exploited in all devices for significant power savings. Voltage reduction can reach 12% in multicore CPUs, 20% in manycore GPUs and 39% in FPGAs.","PeriodicalId":383056,"journal":{"name":"2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132151465","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: 18