Reliability Assessment of Wafer Level Package using Artificial Neural Network Regression Model

2019 ◽  
Vol 35 (6) ◽  
pp. 829-837 ◽  
Author(s):  
P. H. Chou ◽  
K.N. Chiang ◽  
Steven Y. Liang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Electronic Packaging ◽  
Design Parameters ◽  
Simulation Technology ◽  
Wafer Level ◽  
Structure Reliability ◽  
Artificial Neural ◽  
Packaging Structure

ABSTRACTFor electronic packaging structure, there are many design parameters that will affect its reliability performance, using experimental way to obtain the reliability result will take a considerable amount of time. Therefore, how to shorten the design time becomes a critical issue for new electronic packaging structure development. This research will combine artificial intelligence (AI) and simulation technology to assess the long-term reliability of wafer level packaging (WLP). A simulation technology using finite element method (FEM) with appropriate mechanics theories has been validated by multiple experiments will replace the experiment to create reliability results for different WLP structures. After a big WLP structure-reliability database created, this study will apply artificial neural network (ANN) theory to analyze this database and obtains a regression model for structure-reliability relationship of WLP. Once the regression model is established and validated, the WLP geometry, such as pad size, die and buffer layer thickness, and solder volume, etc. can be simply entered, and then the WLP reliability results can be immediately obtained through the ANN regression model.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

The Artificial Neural Network and Regression Analysis Approach to Prediction of the Fiber Diameter of Spunbonding Fabrics

2010 ◽  
Vol 33 ◽  
pp. 74-78
Author(s):  
B. Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Statistical Regression ◽  
Artificial Neural

In this work, the artificial neural network model and statistical regression model are established and utilized for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, which is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical regression model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Md Vaseem Chavhan ◽  
M. Ramesh Naidu ◽  
Hayavadana Jamakhandi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Regression Models ◽  
Network Models ◽  
Quadratic Regression ◽  
Neural Network Models ◽  
Content Type ◽  
Sewing Thread ◽  
Artificial Neural

Purpose This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301. Design/methodology/approach In the present study, the generalized regression and neural network models are developed by considering the fabric types: woven, nonwoven and multilayer combination thereof, with basic sewing parameters: sewing thread linear density, stitch density, needle count and fabric assembly thickness. The network with feed-forward backpropagation is considered to build the ANN, and the training function trainlm of MATLAB software is used to adjust weight and basic values according to the optimization of Levenberg Marquardt. The performance of networks measured in terms of the mean squared error and the layer output is set according to the sigmoid transfer function. Findings The proposed ANN and regression model are able to predict the thread consumption with more accuracy for multilayered seam assembly. The predictability of thread consumption from available geometrical models, regression models and industrial empirical techniques are compared with proposed linear regression, quadratic regression and neural network models. The proposed quadratic regression model showed a good correlation with practical thread consumption value and more accuracy in prediction with an overall 4.3% error, as compared to other techniques for given multilayer substrates. Further, the developed ANN network showed good accuracy in the prediction of thread consumption. Originality/value The estimation of thread consumed while stitching is the prerequisite of the garment industry for inventory management especially with the introduction of the costly high-performance sewing thread. In practice, different types of fabrics are stitched at multilayer combinations at different locations of the stitched product. The ANN and regression models are developed for multilayered seam assembly of woven and nonwoven fabric blend composition for better prediction of thread consumption.

scholarly journals A critical evaluation of the methods for the determination of required volumes for detention tank

2018 ◽  
Vol 45 ◽  
pp. 00088 ◽  
Author(s):  
Mariusz Starzec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Catchment Area ◽  
Critical Evaluation ◽  
Design Parameters ◽  
Method Of Calculation ◽  
Rainfall Duration ◽  
Detention Tank ◽  
Artificial Neural

Simplified methods allow a straightforward and quick determination of parameters of interest. A simplified method of calculation to be used must provide sufficiently accurate simulation results. This paper presents the results of tests completed to evaluate the effects of the parameters which describe a sewer catchment area and network on the value of Tp, a parameter applied in the Dziopak method [18]. The results of 2997 hydrodynamic simulations allowed to formulate an artificial neural network the application of which enabled the determination of the value of Tp dependent on the design parameters of a sewer catchment area and network. The artificial neural network had a very low error R2 = 0.9972 between the expected and determined values of Tp. The completed tests indicated a relationship by which an increase of the rainfall duration, a parameter used in the dimensioning of detention tank, is concomitant to an increase in the value of Tp. The calculations made so far included an assumption that the Tp value is constant irrespective of the design rainfall duration for the dimensioning of detention tank; this assumption has led to gross calculation errors. The paper also provides proof that the inclusion of these relationships allows a more precise determination of the service volume required for a multi-chamber detention tank.

CFD-based blade shape optimization of MGT-70(3)axial flow compressor

2019 ◽  
Vol 30 (6) ◽  
pp. 3307-3321 ◽  
Author(s):  
Mohammad Reza Pakatchian ◽  
Hossein Saeidi ◽  
Alireza Ziamolki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shape Optimization ◽  
Fluid Dynamic ◽  
Axial Compressor ◽  
Axial Flow ◽  
Design Parameters ◽  
Content Type ◽  
Wide Range ◽  
Artificial Neural

Purpose This study aims at enhancing the performance of a 16-stage axial compressor and improving the operating stability. The adopted approaches for upgrading the compressor are artificial neural network, optimization algorithms and computational fluid dynamics. Design/methodology/approach The process starts with developing several data sets for certain 2D sections by means of training several artificial neural networks (ANNs) as surrogate models. Afterward, the trained ANNs are applied to the 3D shape optimization along with parametrization of the blade stacking line. Specifying the significant design parameters, a wide range of geometrical variations are considered by implementation of appropriate number of design variables. The optimized shapes are analyzed by applying computational fluid dynamic to obtain the best geometry. Findings 3D optimal results show improvements, especially in the case of decreasing or elimination of near walls corner separations. In addition, in comparison with the base geometry, numerical optimization shows an increase of 1.15 per cent in total isentropic efficiency in the first four stages, which results in 0.6 per cent improvement for the whole compressor, even while keeping the rest of the stages unchanged. To evaluate the numerical results, experimental data are compared with obtained data from simulation. Based on the results, the highest absolute relative deviation between experimental and numerical static pressure is approximately 7.5 per cent. Originality/value The blades geometry of an axial compressor used in a heavy-duty gas turbine is optimized by applying artificial neural network, and the results are compared with the base geometry numerically and experimentally.

Concrete strength prediction using artificial neural network and genetic programming

2018 ◽  
Vol 9 (3) ◽  
pp. 75
Author(s):  
Preeti Kulkarni ◽  
Shreenivas N. Londhe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Genetic Programming ◽  
Construction Material ◽  
Concrete Strength ◽  
Maximum Size ◽  
Design Parameters ◽  
Ann Model ◽  
Influencing Parameters ◽  
Artificial Neural

Concrete is a highly complex composite construction material and modeling using computing tools to predict concrete strength is a difficult task. In this work an effort is made to predict compressive strength of concrete after 28 days of curing, using Artificial Neural Network (ANN) and Genetic programming (GP). The data for analysis mainly consists of mix design parameters of concrete, coefficient of soft sand and maximum size of aggregates as input parameters. ANN yields trained weights and biases as the final model which sometime may impediment in its application at operational level. GP on other hand yields an equation as its output making its plausible tool for operational use. Comparison of the prediction results displays the result the model accuracy of both ANN and GP as satisfactory, giving GP a working advantage owing to its output in an equation form. A knowledge extraction technique used with the weights and biases of ANN model to understand the most influencing parameters to predict the 28 day strength of concrete, promises to prove ANN as grey box rather than a black box. GP models, in form of explicit equations, show the influencing parameters with reference to the presence of the relevant parameters in the equations.

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