Thermal-mechanical reliability assessment of TSV structure for 3D IC integration

Author(s):  
Huan Liu ◽  
Qinghua Zeng ◽  
Yong Guan ◽  
Runiu Fang ◽  
Xin Sun ◽  
...  
Author(s):  
Tiantao Lu ◽  
Ankur Srivastava

This paper presents an electrical-thermal-reliability co-design technique for TSV-based 3D-ICs. Although TSV-based 3D-IC shows significant electrical performance improvement compared to traditional 2D circuit, researchers have reported strong electromigration (EM) in TSVs, which is induced by the thermal mechanical stress and the local temperature hotspot. We argue that rather than addressing 3D-IC’s EM issue after the IC designing phase, the designer should be aware of the circuit’s thermal and EM properties during the IC designing phase. For example, one should be aware that the TSVs establish vertical heat conduction path thus changing the chip’s thermal profile and also produce significant thermal mechanical stress to the nearby TSVs, which deteriorates other TSV’s EM reliability. Therefore, the number and location of TSVs play a crucial role in deciding 3D-IC’s electrical performance, changing its thermal profile, and affecting its EM-reliability. We investigate the TSV placement problem, in order to improve 3D-IC’s electrical performance and enhance its thermal-mechanical reliability. We derive and validate simple but accurate thermal and EM models for 3D-IC, which replace the current employed time-consuming finite-element-method (FEM) based simulation. Based on these models, we propose a systematic optimization flow to solve this TSV placement problem. Results show that compared to conventional performance-centered technique, our design methodology achieves 3.24x longer EM-lifetime, with only 1% performance degradation.


Vehicles ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 126-141
Author(s):  
Weizhen You ◽  
Alexandre Saidi ◽  
Abdel-malek Zine ◽  
Mohamed Ichchou

Reliability assessment plays a significant role in mechanical design and improvement processes. Uncertainties in structural properties as well as those in the stochatic excitations have made reliability analysis more difficult to apply. In fact, reliability evaluations involve estimations of the so-called conditional failure probability (CFP) that can be seen as a regression problem taking the structural uncertainties as input and the CFPs as output. As powerful ensemble learning methods in a machine learning (ML) domain, random forest (RF), and its variants Gradient boosting (GB), Extra-trees (ETs) always show good performance in handling non-parametric regressions. However, no systematic studies of such methods in mechanical reliability are found in the current published research. Another more complex ensemble method, i.e., Stacking (Stacked Generalization), tries to build the regression model hierarchically, resulting in a meta-learner induced from various base learners. This research aims to build a framework that integrates ensemble learning theories in mechanical reliability estimations and explore their performances on different complexities of structures. In numerical simulations, the proposed methods are tested based on different ensemble models and their performances are compared and analyzed from different perspectives. The simulation results show that, with much less analysis of structural samples, the ensemble learning methods achieve highly comparable estimations with those by direct Monte Carlo simulation (MCS).


2014 ◽  
Vol 57 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Moongon Jung ◽  
Joydeep Mitra ◽  
David Z. Pan ◽  
Sung Kyu Lim

10.29007/m56l ◽  
2018 ◽  
Author(s):  
Orazio Giustolisi

Mechanical reliability refers to the assessment of the capacity of the water distribution network (WDN) to provide a correct service to the different type of costumers under abnormal operating conditions due to a failure of a system component. It depends on the effectiveness of the isolation valve system (IVS) and on the failure probability of components. Starting from the calculation of the actual customer demands during abnormal operating conditions of the hydraulic systems due to valve shutdowns and the failure probability of the separated segments, the work develops a metric for WDN reliability assessment. The finding is that the topologic part of WDN reliability assessment, relating to the IVS, is based on the risk of disconnection. Starting from it, the works develops a special modularity index for IVS reliability assessment.


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