Title :
Neurogenetic design centering
Author :
Pratap, Rana J. ; Sen, Padmanava ; Davis, Cleon E. ; Mukhophdhyay, Rajarshi ; May, Gary S. ; Laskar, Joy
Author_Institution :
Intel Corp., Chandler, AZ, USA
fDate :
5/1/2006 12:00:00 AM
Abstract :
A new technique for design centering and yield enhancement of devices and circuits is presented. The proposed method uses neural networks for device and/or circuit modeling and genetic algorithms for parametric yield optimization. It uses a Monte Carlo-based method for yield estimation via the neural models (thus consuming less time) and genetic algorithms for efficient design centering. The neurogenetic methodology has been used for design centering of SiGe heterojunction transistors and millimeter-wave voltage controlled oscillators. It results in significant yield enhancement of the SiGe heterojunction bipolar transistors (from 25% to 75%) and voltage controlled oscillators (from 8 % to 85 %). To the best of our knowledge, this method has not been reported previously.
Keywords :
Ge-Si alloys; Monte Carlo methods; circuit optimisation; genetic algorithms; heterojunction bipolar transistors; millimetre wave oscillators; neural nets; voltage-controlled oscillators; Monte Carlo method; SiGe; circuit modeling; device modeling; genetic algorithms; heterojunction bipolar transistors; millimeterwave oscillators; neural models; neural networks; neurogenetic design centering; parametric yield optimization; voltage controlled oscillators; yield enhancement; yield estimation; Algorithm design and analysis; Circuits; Genetic algorithms; Germanium silicon alloys; Heterojunctions; Neural networks; Optimization methods; Silicon germanium; Voltage-controlled oscillators; Yield estimation; Genetic algorithms (GA); Monte Carlo method; neural networks; parametric yield estimation;
Journal_Title :
Semiconductor Manufacturing, IEEE Transactions on
DOI :
10.1109/TSM.2006.873517