Validation of new approach of modelling traces by mapping mechanical properties for a printed circuit board mechanical analysis

Yaddanapudi, Vamsi Krishna; Krishnaswamy, Sivasubramani; Rath, Rajiv; Gandhi, Rohith

doi:10.1109/eptc.2015.7412355

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…The third one, introduced by Gibson [ 30 ] and Lee et al [ 29 ], is a simulation-based approach that calculates the anisotropic equivalent material properties of blocks containing complex traces. To achieve higher accuracy, Yaddanapudi [ 31 ] divided the RDL into extremely small-size pixels and directly defined each pixel as either metal or dielectric; a high-fidelity structural response of RDL was achieved by consuming a large amount of solving resources. In general, existing work indicates that there is a trade-off between division sizes and the material equivalization method.…”

Section: Introductionmentioning

confidence: 99%

An RDL Modeling and Thermo-Mechanical Simulation Method of 2.5D/3D Advanced Package Considering the Layout Impact Based on Machine Learning

Wu,

Wang,

et al. 2023

Micromachines

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The decreasing-width, increasing-aspect-ratio RDL presents significant challenges to the design for reliability (DFR) of an advanced package. Therefore, this paper proposes an ML-based RDL modeling and simulation method. In the method, RDL was divided into blocks and subdivided into pixels of metal percentage, and the RDL was digitalized as tensors. Then, an ANN-based surrogate model was built and trained using a subset of tensors to predict the equivalent material properties of each block. Lastly, all blocks were transformed into elements for simulations. For validation, line bending simulations were conducted on an RDL, with the reaction force as an accuracy indicator. The results show that neglecting layout impact caused critical errors as the substrate thinned. According to the method, the reaction force error was 2.81% and the layout impact could be accurately considered with 200 × 200 elements. For application, the TCT maximum temperature state simulation was conducted on a CPU chip. The simulation indicated that for an advanced package, the maximum stress was more likely to occur in RDL rather than in bumps; both RDL and bumps were critically impacted by layouts, and RDL stress was also impacted by vias/bumps. The proposed method precisely concerned layout impacts with few resources, presenting an opportunity for efficient improvement.

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Section: Introductionmentioning

confidence: 99%

An RDL Modeling and Thermo-Mechanical Simulation Method of 2.5D/3D Advanced Package Considering the Layout Impact Based on Machine Learning

Wu,

Wang,

et al. 2023

Micromachines

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show abstract

“…Though this investigation is reliable and comprehensive, it is quite complicated, tedious, and cost-ineffective to implement for real applications. Later, an efficient and robust approach based on the trace mapping and modeling (TMM) technique and the FEA-based effective method was introduced with great success to macroscopically model circuit laminates, such as IC substrates [7,25] or PCBs [26]. The TMM technique is responsible for mapping each of the metal circuit planes in a circuit laminate onto a prebuilt 3D regular and fine mesh.…”

Section: Introductionmentioning

confidence: 99%

Effective Macroscopic Thermomechanical Characterization of Multilayer Circuit Laminates for Advanced Electronic Packaging

Cheng,

Jhu

2023

Materials

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Laminate substrates in advanced IC packages serve as not only the principal heat dissipation pathway but also the critical component governing the thermomechanical performance of advanced packaging technologies. A solid and profound grasp of their thermomechanical properties is of crucial importance to better understand IC packages’ thermomechanical behavior. This study attempts to introduce a subregion homogenization modeling framework for effectively and efficiently modeling and characterizing the equivalent thermomechanical behavior of large-scale and high-density laminate substrates comprising the non-uniform distribution and non-unidirectional orientation of tiny metal traces. This framework incorporates subregion modeling, trace mapping and modeling, and finite element analysis (FEA)-based effective modeling. In addition, the laminates are macroscopically described as elastic orthotropic or elastic anisotropic material. This framework is first validated with simple uniaxial tensile and thermomechanical test simulations, and the calculation results associated with these two effective material models are compared with each other, as well as with those of two existing mixture models, and direct the detailed FEA. This framework is further tested on the prediction of the process-induced warpage of a flip chip chip-scale package, and the results are compared against the measurement data and the results of the whole-domain modeling-based effective approach and two existing mixture models.

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“…The result is an anisotropic, spatially-varying, and temperature-dependent material that is cumbersome to characterize [3], [4], tedious to calculate analytically [5]- [7] and complex to model [8]. Several authors have demonstrated that FEA simulations with substrates modelled using trace mapping is the best method to model warpage accurately [9]- [13]. Trace mapping works by first importing the electrical artwork and then 'superimposing' it onto the elements of the metal layer in the FEA model.…”

Section: Introductionmentioning

confidence: 99%

Learning Localized Spatial Material Properties of Substrates in Ultra-Thin Packages Using Markov Chain Monte Carlo and Finite Element Analysis

2020

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Thinned silicon dies and thin substrates using thin core and coreless structures have enabled thin packages. For robust manufacturing and reliability of these parts, solving the warpage problem is key. While current finite element methodologies can provide some insights at the design stage, these simulations are only as accurate as the inputs such as the material properties and the stress-free temperatures. Electronic substrates are especially challenging to characterize and model as they are laminates consisting of a core with layers of resin and metal lines on either side. In this work, a hybrid approach using Markov Chain Monte Carlo (MCMC) and Finite Element Analysis (FEA) is used to learn the spatially varying properties of the substrate from Digital Image Correlation (DIC) measurements of the warpage. The analysis is carried out at room temperature and at an elevated temperature point. Image analysis on electrical artwork is also carried out to correlate the material properties to the substrate metal density. These results will be useful to package and substrate designers to understand how material properties vary over the substrate and how temperature and metal density affect material properties so that robust design for future packages to minimize warpage can be initiated by careful routing of metal lines depending on the locally desired properties of the stack. INDEX TERMS Bayes method, finite element analysis, Markov Chain Monte Carlo (MCMC), material characterization, substrate, warpage.

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Validation of new approach of modelling traces by mapping mechanical properties for a printed circuit board mechanical analysis

Cited by 7 publications

References 5 publications

An RDL Modeling and Thermo-Mechanical Simulation Method of 2.5D/3D Advanced Package Considering the Layout Impact Based on Machine Learning

An RDL Modeling and Thermo-Mechanical Simulation Method of 2.5D/3D Advanced Package Considering the Layout Impact Based on Machine Learning

Effective Macroscopic Thermomechanical Characterization of Multilayer Circuit Laminates for Advanced Electronic Packaging

Learning Localized Spatial Material Properties of Substrates in Ultra-Thin Packages Using Markov Chain Monte Carlo and Finite Element Analysis

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