• Sensitivity analysis and optim...

Result: Sensitivity analysis and optimization of excimer laser ablation for microvia formation using neural networks and genetic algorithms

Title:
Sensitivity analysis and optimization of excimer laser ablation for microvia formation using neural networks and genetic algorithms
Authors:
SETIA, Ronald, MAY, Gary S, SUNDARAM, Venky, TUMMALA, Rao R, HYOUNG HO ROH
Source:
29th international electronics manufacturing technology symposium (San Jose CA CA, 14-16 July 2004). :131-139
Publisher Information:
Piscataway NJ: IEEE, 2004.
Publication Year:
2004
Physical Description:
print, 19 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Electronics, Electronique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Electronique, Electronics, Electronique des semiconducteurs. Microélectronique. Optoélectronique. Dispositifs à l'état solide, Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices, Circuits intégrés, Integrated circuits, Conception. Technologies. Analyse fonctionnement. Essais, Design. Technologies. Operation analysis. Testing, Circuits électriques, optiques et optoélectroniques, Electric, optical and optoelectronic circuits, Réseaux neuronaux, Neural networks, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse sensibilité, Sensitivity analysis, Análisis sensibilidad, Conditions opératoires, Process conditions, Condiciones operatorias, Câblage, Wiring, Colocación cables, Laser excimère, Excimer lasers, Méthode ablation laser, Laser ablation technique, Optimisation, Optimization, Optimización, Packaging électronique, Electronic packaging, Packaging electrónico, Propagation erreur, Growth of error, Propagación error, Réseau neuronal non bouclé, Feedforward neural nets, Réseau neuronal, Neural network, Red neuronal, Trou interconnexion, Via hole, Agujero interconexión
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Electrical and Computer Engineering Packaging Research Center Georgia Institute of Technology, Atlanta, Georgia 30332 - 0250, United States
Samsung Techwin Co. Ltd, Korea, Republic of
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18162556
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Electronics
Accession Number:
edscal.18162556
Database:
PASCAL Archive

Further Information

Higher levels of integration on the chip and package with increasing wiring density require highly repeatable and reliable microvias. To meet future needs of local via densities >5000 vias per cm2 and via diameters of <50 μm, a scanning projection excimer laser ablation process is being developed for build-up and flex substrates. Vias with diameters of 30, 40, and 50 μm are ablated in 25-μm thick DuPont Kapton® E polyimide using an Anvik HexScan<TM> 2150 SXE excimer laser. A 25-1 fractional factorial experiment is conducted to determine the significance of laser fluence, shot frequency, number of pulses, and vertical and horizontal positions of the debris removal system in the laser tool in affecting ablated dielectric thickness, top via diameter, via wall angle, and via resistance. The complex nonlinear interactions between process set points and responses are empirically modeled using the feed-forward neural networks (NNs) employing the error back-propagation training algorithm. Neural networks encode the functional relationship between process conditions and responses, and are then used to perform sensitivity analysis to quantify the variation in responses for incremental changes in particular process conditions. In addition, genetic algorithms (GAs) are used to identify optimized recipes for neural network response models. Experimental verification of the optimized recipes is performed to achieve completely open microvias, specific microvia diameters and wall angles, and low via resistance.