Machine Learning-Based Local Sensitivity Analysis of Integrated Circuits to Process Variations

被引：0
|
作者：
Sandru, Elena-Diana [1 ]
David, Emilian [2 ]
Pelz, Georg [2 ]
机构：
[1] Univ Politehn Bucuresti, Bucharest, Romania
[2] Infineon Technol, Bucharest, Romania
来源：
2020 27TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS (ICECS) | 2020年
关键词：
Local Sensitivity Analysis; Process Control Monitor parameters; Corner Lots; Product Characterization;
D O I：
10.1109/icecs49266.2020.9294956
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents a local sensitivity analysis methodology of circuit performances, i.e. Electrical Parameters (EPs), with manufacturing process variations, based on modelling the EPs dependence on the Process Control Monitor parameters. It can be used as an assisting tool for the designer, during the product characterization.
引用
收藏
页数：2
相关论文
共 50 条
  • [21] Predictive machine learning-based integrated approach for DDoS detection and prevention
    Kebede, Solomon Damena
    Tiwari, Basant
    Tiwari, Vivek
    Chandravanshi, Kamlesh
    MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (03) : 4185 - 4211
  • [22] Machine Learning-Based Integrated Wireless Sensing and Positioning for Cellular Network
    Zhang, Lei
    Chu, Xin
    Zhai, Menglin
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 72
  • [23] Machine Learning-Based Integrated Wireless Sensing and Positioning for Cellular Network
    Zhang, Lei
    Chu, Xin
    Zhai, Menglin
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 72
  • [24] Machine Learning-Based Integrated Wireless Sensing and Positioning for Cellular Network
    Zhang, Lei
    Chu, Xin
    Zhai, Menglin
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 72
  • [25] Machine learning-based models for genomic predicting neoadjuvant Machine learning-based models for genomic predicting neoadjuvant chemotherapeutic sensitivity in cervical cancer chemotherapeutic sensitivity in cervical cancer
    Guo, Lu
    Wang, Wei
    Xie, Xiaodong
    Wang, Shuihua
    Zhang, Yudong
    BIOMEDICINE & PHARMACOTHERAPY, 2023, 159
  • [26] Machine Learning-based Software Effort Estimation : An Analysis
    Polkowski, Zdzislaw
    Vora, Jayneel
    Tanwar, Sudeep
    Tyagi, Sudhanshu
    Singh, Pradeep Kumar
    Singh, Yashwant
    PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI-2019), 2019,
  • [27] Machine learning-based analysis of adolescent gambling factors
    Seo, Wonju
    Kim, Namho
    Lee, Sang-Kyu
    Park, Sung-Min
    JOURNAL OF BEHAVIORAL ADDICTIONS, 2020, 9 (03) : 734 - 743
  • [28] MACHINE LEARNING-BASED ANALYSIS OF ENGLISH LATERAL ALLOPHONES
    Piotrowska, Magdalena
    Korvel, Grazina
    Kostek, Bozena
    Ciszewski, Tomasz
    Czyzewski, Andrzej
    INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE, 2019, 29 (02) : 393 - 405
  • [29] Machine Learning-Based Framework for the Analysis of Project Viability
    Tshimula, Jean Marie
    Togashi, Atsushi
    PROCEEDINGS OF 2018 3RD INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEMS (ICCCS), 2018, : 80 - 84
  • [30] Machine Learning-Based Queueing Time Analysis in XGPON
    Ismail, N. A.
    Idrus, S. M.
    Iqbal, F.
    Zin, A. M.
    Atan, F.
    Ali, N.
    INTERNATIONAL JOURNAL OF NANOELECTRONICS AND MATERIALS, 2021, 14 : 157 - 163
12345