A hierarchy preserving hierarchical compactor
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
A VLSI artwork legalization technique based on a new criterion of minimum layout perturbation
Proceedings of the 1997 international symposium on Physical design
HIMALAYAS — a hierarchical compaction system with a minimized constraint set
ICCAD '92 Proceedings of the 1992 IEEE/ACM international conference on Computer-aided design
Application of automated design migration to alternating phase shift mask design
Proceedings of the 2001 international symposium on Physical design
A Fast Minimum Layout Perturbation Algorithm for Electromigration Reliability Enhancement
DFT '98 Proceedings of the 13th International Symposium on Defect and Fault-Tolerance in VLSI Systems
Efficient Algorithms for Integer Programs with Two Variables per Constraint
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
An algorithm to compact a VLSI symbolic layout with mixed constraints
DAC '83 Proceedings of the 20th Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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The integer minimum perturbation problem with sum and difference constraints is stated as follows: minimize f(x)=a1 x1 b 1 +a 2-b2 +…+an xn-b n under constraints ±xi1±xj1≥c1, ±xi2±xj2≥c2, … … … ±xim±xjm≥cm, where the sign in front of each variable is either "+" or "-", a1; a2…an≥0 and all variables and constants are integers.The minimum perturbation problem [6] arose from layout migration. The sum and difference constraints arose from the hierarchical nature of the layout. We proposed and implemented a graph based algorithm to solve this optimization problem. Our algorithm consists of two steps. First find the optimal solution for the non-integer version of the problem by using a modification of simplex method which takes advantage of the special form of the constraints (a graph based simplex method). Then find an integer solution close to the optimal by solving a 2-SAT problem. The time complexity of the algorithm is O(p(m + n), where p is the number of pivots in the simplex algorithm; note that the regular simplex method, being applied to this problem, would require O(pn(m + n)) time.Our result on production layouts shows that the runtime scale very well with a O(nlog(n)) scanline algorithm used to generate the constraints for the layouts. This makes it a very practical solver for the problem.