Monte Carlo Statistical Tolerance Analysis of a Parallel-Plate Multichip Power Module

被引:1
|
作者
Lester, Danielle [1 ]
Cairnie, Mark [1 ]
DiMarino, Christina [1 ]
机构
[1] Ctr Power Elect Syst, Bradley Dept Elect & Comp Engn, Arlington, VA 22203 USA
关键词
Tolerance analysis; Assembly; Monte Carlo methods; Substrates; Silicon carbide; Integrated circuit interconnections; Silicon; Monte Carlo; multichip power module; nano-silver sintering; post interconnects; silicon carbide; statistical tolerance analysis; IGBT MODULES; SIC-MOSFET; RELIABILITY; PERFORMANCE; DESIGN;
D O I
10.1109/TPEL.2024.3439473
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
A Monte Carlo statistical tolerance analysis is conducted on a parallel-plate, wirebond-less multichip power module (MCPM) to analyze the planarity across modules with post interconnects and identify which parts in the assembly contribute the most variation. Power electronics packaging has migrated from wirebonds to post interconnects that allow for vertical, parallel-plate modules, improving power density, reducing power loop inductances, and enabling double-sided cooled modules. Demonstrations of MCPMs of this structure have been limited in the number of die. This is a substantial gap in literature since as the number of die in parallel increases, the number of post interconnects and bondlines scale, resulting in more variation across component heights and reducing the yield and probability of connecting every interconnect. Different tolerance analyses are discussed and analyzed for the assembly of the MCPM, and a Monte Carlo statistical tolerance analysis is conducted to quantify the maximum height mismatch across the module and identify, which components introduce the most variation. Three MCPMs are fabricated using the statistical tolerance analysis, targeting components to reduce the maximum height mismatch. The 13 kV, six-die module is successfully characterized and is the first functional module of its kind.
引用
收藏
页码:16078 / 16090
页数:13
相关论文
共 50 条
  • [21] MPI Parallel Monte Carlo Framework for the Reliability Analysis of Highway Bridges
    Fiorillo, Graziano
    Ghosn, Michel
    JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2018, 32 (02)
  • [22] A fast Monte Carlo ion implantation simulation based on statistical enhancement technique and parallel computation
    Hane, M
    Ikezawa, T
    Matsumoto, H
    NEC RESEARCH & DEVELOPMENT, 1996, 37 (02): : 170 - 178
  • [23] Statistical Power in Two-Level Models: A Tutorial Based on Monte Carlo Simulation
    Arend, Matthias G.
    Schaefer, Thomas
    PSYCHOLOGICAL METHODS, 2019, 24 (01) : 1 - 19
  • [24] Research on Reliability Evaluation of Power System Including Improved Monte Carlo and Parallel Calculation
    Li, Na
    Zhu, Zhenhua
    Li, Ming
    Lin, Ying
    Wang, Xiaoliang
    Liu, Qinglong
    PROCEEDINGS OF 2017 2ND INTERNATIONAL CONFERENCE ON POWER AND RENEWABLE ENERGY (ICPRE), 2017, : 534 - 538
  • [25] Combined interval analysis - Monte Carlo simulation approach for the analysis of uncertainties in parallel manipulators
    Hiparco Lins Vieira
    André Teófilo Beck
    Maíra Martins da Silva
    Meccanica, 2021, 56 : 1867 - 1881
  • [26] Markov and Monte Carlo Simulation of Waste-to-Energy Power Plants Considering Variable Fuel Analysis and Failure Rates
    Behbahaninia, Ali
    Banifateme, Mohsen
    Azmayesh, Mohammad Hasan
    Naderi, Shayan
    Pignatta, Gloria
    JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME, 2022, 144 (06):
  • [27] Risk analysis of power plant investment based on Monte Carlo
    Niu, Dongxiao
    Qiu, Jingpeng
    Wu, Meiqiong
    Huang, Yali
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND INNOVATIVE EDUCATION (MSIE 2015), 2015, 32 : 275 - 279
  • [28] Developing and investigating a pure Monte-Carlo module for transient neutron transport analysis
    Mylonakis, Antonios G.
    Varvayanni, M.
    Grigoriadis, D. G. E.
    Catsaros, N.
    ANNALS OF NUCLEAR ENERGY, 2017, 104 : 103 - 112
  • [29] Monte Carlo based sensitivity and uncertainty analysis of the HCPB Test Blanket Module in ITER
    Fischer, U.
    Leichtle, D.
    Perel, R. L.
    FUSION ENGINEERING AND DESIGN, 2008, 83 (7-9) : 1222 - 1226
  • [30] Uncertainty quantification analysis and statistical estimation for LBLOCA in a PWR using Monte-Carlo and alternative methods
    Kang, Dong Gu
    ANNALS OF NUCLEAR ENERGY, 2021, 150