A Space–Time Neural Network for Analysis of Stress Evolution Under DC Current Stressing

Hou, Tianshu; Wong, Ngai; Chen, Quan; Ji, Zhigang; Chen, Hai-Bao

doi:10.1109/tcad.2022.3166103

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…One limitation of employing these competing learningbased schemes in EM analysis is that the prediction accuracy will decrease as the number of segments increases since the methods focus on solving a single PDE and cannot directly provide a global approximation for the interconnects governed by coupled PDEs subject to complex BCs. To mitigate this problem, our previous work [25] extended PINN to a new space-time physics-informed neural network (STPINN) for analyzing the EM-induced stress evolution by coupling the physics-based EM analysis with dynamic temperature incorporating Joule heating and via effect.…”

Section: Interconnect Treementioning

confidence: 99%

Multilayer Perceptron Based Stress Evolution Analysis under DC Current Stressing for Multi-segment Wires

Hou¹,

Zhen²,

Wong³

et al. 2022

Preprint

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Electromigration (EM) is one of the major concerns in the reliability analysis of very large scale integration (VLSI) systems due to the continuous technology scaling. Accurately predicting the time-to-failure of integrated circuits (IC) becomes increasingly important for modern IC design. However, traditional methods are often not sufficiently accurate, leading to undesirable over-design especially in advanced technology nodes. In this paper, we propose an approach using multilayer perceptrons (MLP) to compute stress evolution in the interconnect trees during the void nucleation phase. The availability of a customized trial function for neural network training holds the promise of finding dynamic mesh-free stress evolution on complex interconnect trees under time-varying temperatures. Specifically, we formulate a new objective function considering the EMinduced coupled partial differential equations (PDEs), boundary conditions (BCs), and initial conditions to enforce the physicsbased constraints in the spatial-temporal domain. The proposed model avoids meshing and reduces temporal iterations compared with conventional numerical approaches like FEM. Numerical results confirm its advantages on accuracy and computational performance.

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Section: Interconnect Treementioning

confidence: 99%

Multilayer Perceptron Based Stress Evolution Analysis under DC Current Stressing for Multi-segment Wires

Hou¹,

Zhen²,

Wong³

et al. 2022

Preprint

Self Cite

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“…Chen et al (2021) investigated the fatigue life of wafer-level chip scale packaging (WLCSP) using a hybrid finite element-neural network approach and developed a feasible finite element-artificial neural network (FEM-ANN) hybrid method to solve finite element problems in batch. Hou et al (2022) proposed a novel meshless model for computing stress evolution induced by electromigration in ultra-large-scale integrated circuits, which eliminates the need for time discretization and grid generation in traditional numerical stress evolution analysis, thereby saving computation time while ensuring satisfactory accuracy. Zippelius et al (2022) investigated the thermal-mechanical fatigue of different Sn-Ag-Cu (SAC) solders by transient thermal analysis (TTA), and predicted their thermal-mechanical fatigue behavior using ANNs.…”

Section: Introductionmentioning

confidence: 99%

Temperature and current density prediction in solder joints using artificial neural network method

Liu,

Xu,

et al. 2023

SSMT

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Purpose Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints. Design/methodology/approach The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data. Findings Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%. Originality/value The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.

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PostPINN-EM: Fast Post-Voiding Electromigration Analysis Using Two-Stage Physics-Informed Neural Networks

Lamichhane,

Jin,

Chen

et al. 2023

2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)

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A Space–Time Neural Network for Analysis of Stress Evolution Under DC Current Stressing

Cited by 8 publications

References 40 publications

Multilayer Perceptron Based Stress Evolution Analysis under DC Current Stressing for Multi-segment Wires

Multilayer Perceptron Based Stress Evolution Analysis under DC Current Stressing for Multi-segment Wires

Temperature and current density prediction in solder joints using artificial neural network method

PostPINN-EM: Fast Post-Voiding Electromigration Analysis Using Two-Stage Physics-Informed Neural Networks

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