By Ricardo Lourenço, Nuno Lourenço, Nuno Horta
This paintings addresses the learn and improvement of an leading edge optimization kernel utilized to analog built-in circuit (IC) layout. fairly, this works describes the variations contained in the AIDA Framework, an digital layout automation framework absolutely constructed by means of on the built-in Circuits Group-LX of the Instituto de Telecomunicações, Lisbon. It focusses on AIDA-CMK, via bettering AIDA-C, that is the circuit optimizer portion of AIDA, with a brand new multi-objective multi-constraint optimization module that constructs a base for a number of set of rules implementations. The proposed resolution implements 3 techniques to multi-objective multi-constraint optimization, specifically, an evolutionary process with NSGAII, a swarm intelligence strategy with MOPSO and stochastic hill mountain climbing strategy with MOSA. furthermore, the carried out constitution permits the straightforward hybridization among kernels reworking the former uncomplicated NSGAII optimization module right into a extra advanced and flexible module aiding a number of unmarried and multi-kernel algorithms. the 3 multi-objective optimization methods have been confirmed with CEC2009 benchmarks to restricted multi-objective optimization and established with actual analog IC layout difficulties. The accomplished effects have been in comparison when it comes to functionality, utilizing statistical effects acquired from a number of self reliant runs. eventually, a few hybrid ways have been additionally experimented, giving a foretaste to quite a lot of possibilities to discover in destiny work.
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Additional info for AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to Analog IC Sizing
It is not © The Author(s) 2015 R. 1007/978-3-319-15955-3_6 51 52 6 Results for Analog IC Design intended to be fabricated. It is used to simulate, load, and provide the means to measure the performance ﬁgures of the circuit under test. Two analog differential ampliﬁer circuits, a single-stage differential ampliﬁer that uses voltage combiners to boost the gain, and a two-stage miller ampliﬁer are used to evaluate the performance of the various optimization methods. In the case of the single-stage ampliﬁer, a typical 6 pF all capacitive load was considered, while in the two-stage miller ampliﬁer was loaded with a 10 M resistor in parallel with 1 pF capacitor and biased with a current of 10 µA.
The ﬁtness should correspond to an evaluation of how good the candidate solution is. Selection compares each individual in the population by using a ﬁtness function. The new individuals’ ﬁtness is evaluated and, then, they are ranked together with the parents. The ﬁttest individuals are selected as the new parents, and the less ﬁt discarded. 1. NSGA-II uses Pareto dominance concepts to sort the multi-objective solutions. 3, as a tie breaker for solutions with the same rank. 2 illustrates this procedure.
The gene pool should be as large as possible so that any solution of the search space can be engendered but generally, the initial population is generated randomly. Then, the genetic algorithm evolves the solutions by applying the genetic operators and then selecting the next parents. The process is repeated until the convergence or ending criterion is reached. The algorithm is stopped when the population converges toward the optimal solution. New solution vectors are obtained from the current population by the application of the genetic operators of mutation and crossover.