Title
An efficient bi-objective optimization framework for statistical chip-level yield analysis under parameter variations.
Abstract
With shrinking technology, the increase in variability of process, voltage, and temperature (PVT) parameters significantly impacts the yield analysis and optimization for chip designs. Previous yield estimation algorithms have been limited to predicting either timing or power yield. However, neglecting the correlation between power and delay will result in significant yield loss. Most of these approaches also suffer from high computational complexity and long runtime. We suggest a novel bi-objective optimization framework based on Chebyshev affine arithmetic (CAA) and the adaptive weighted sum (AWS) method. Both power and timing yield are set as objective functions in this framework. The two objectives are optimized simultaneously to maintain the correlation between them. The proposed method first predicts the guaranteed probability bounds for leakage and delay distributions under the assumption of arbitrary correlations. Then a power-delay bi-objective optimization model is formulated by computation of cumulative distribution function (CDF) bounds. Finally, the AWS method is applied for power-delay optimization to generate a well-distributed set of Pareto-optimal solutions. Experimental results on ISCAS benchmark circuits show that the proposed bi-objective framework is capable of providing sufficient trade-off information between power and timing yield.
Year
DOI
Venue
2016
10.1631/FITEE.1500168
Frontiers of IT & EE
Keywords
Field
DocType
Parameter variations, Parametric yield, Multi-objective optimization, Chebyshev affine, Adaptive weighted sum, TP312
Mathematical optimization,Computer science,Affine arithmetic,Voltage,Chip,Multi-objective optimization,Cumulative distribution function,Chebyshev filter,Computational complexity theory,Computation
Journal
Volume
Issue
ISSN
17
2
2095-9230
Citations 
PageRank 
References 
1
0.36
23
Authors
4
Name
Order
Citations
PageRank
Xin Li110.36
Jin Sun274.49
Xiao Fu327325.50
Jiangshan Tian440.81