Cart (Loading....) | Create Account
Close category search window
 

Sequential modeling of via formation in photosensitive dielectric materials for MCM-D applications

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Tae Seon Kim ; Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA ; May, G.S.

Multichip module (MCM) technology is considered a strategic solution in electronics packaging because this approach offers significant advantages in electrical and thermal performance and reliability. However, manufacturing cost is a critical issue for mass production of high-performance MCM packages. To realize low-cost manufacturing technology, process modeling, optimization, and control techniques are required. In this paper, a modeling approach for via formation in MCM dielectric layers composed of photosensitive benzocyclobutene (BCB) is presented. A series of designed experiments are used to characterize the via formation workcell (which consists of the spin coat, soft bake, expose, develop, cure, and plasma descum unit process steps). The output characteristics considered are film thickness, refractive index, uniformity, film retention, and via yield. Sequential neural network process models are constructed to characterize the entire process. In the sequential scheme, each workcell subprocess is modeled individually, and each subprocess model is linked to previous subprocess outputs and subsequent subprocess inputs. This modeling scheme is compared with both the global and unit process modeling approaches to evaluate model prediction capability. The sequential method shows superior capability, with an average rms prediction error of 6.40% over all responses, compared to a 11.61% rmse for the global model and a 12.05% error for the unit process models

Published in:

Semiconductor Manufacturing, IEEE Transactions on  (Volume:12 ,  Issue: 3 )

Date of Publication:

Aug 1999

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.