By Ricardo Lourenço, Nuno Lourenço, Nuno Horta
This paintings addresses the examine and improvement of an cutting edge optimization kernel utilized to analog built-in circuit (IC) layout. quite, this works describes the ameliorations contained in the AIDA Framework, an digital layout automation framework absolutely constructed through on the built-in Circuits Group-LX of the Instituto de Telecomunicações, Lisbon. It focusses on AIDA-CMK, via improving AIDA-C, that's the circuit optimizer part 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 answer implements 3 techniques to multi-objective multi-constraint optimization, specifically, an evolutionary method with NSGAII, a swarm intelligence procedure with MOPSO and stochastic hill mountain climbing strategy with MOSA. furthermore, the carried out constitution permits the straightforward hybridization among kernels reworking the former basic NSGAII optimization module right into a extra developed and flexible module assisting a number of unmarried and multi-kernel algorithms. the 3 multi-objective optimization ways have been proven with CEC2009 benchmarks to restricted multi-objective optimization and established with actual analog IC layout difficulties. The accomplished effects have been in comparison by way of functionality, utilizing statistical effects bought from a number of self sufficient runs. ultimately, 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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This paintings addresses the study and improvement of an leading edge optimization kernel utilized to analog built-in circuit (IC) layout. fairly, this works describes the alterations contained in the AIDA Framework, an digital layout automation framework absolutely built through on the built-in Circuits Group-LX of the Instituto de Telecomunicações, Lisbon.
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Extra resources for AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to Analog IC Sizing
F m ð xÞ ¼ pm Àpm 8 p ÀP i i > > jPi j > > < pi gi ð x Þ ¼ > Àpi > > > : Pi Àpi jPi j when minimizing pm when maximizing pm when the constraint is pi ! Pi ð3:2Þ when the constraint is pi ! Pi and Pi ¼ 0 when the constraint is pi Pi and Pi ¼ 0 when the constraint is pi Fig. 4 illustrates the objective and constraint functions for the differential ampliﬁer circuit in Fig. 4. , is faster. Despite the output does not consider the limitations imposed by the extreme variations of process and environment parameters, it is useful to the circuit designer to perform tradeoff analysis.
In: International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, Seville, 2012 3. : Synopsys HSPICE—accurate circuit simulation. com (2014). Accessed 2014 4. : Eldo classic—foundry certiﬁed SPICE accurate circuit simulation. http://www. com/products/ic_nanometer_design/analog-mixed-signal-veriﬁcation/eldo/ (2014). Accessed 2014 5. : LAYGEN II: Analog ICs Layout Generator. Springer, Berlin (2013) 6. : IC veriﬁcation and signoff using calibre.
The implementation of a new algorithm is done in two steps: ﬁrst the enhancement of the optimization element with the operators of the new algorithm, and then the creation of another implementation of the abstract optimizer. By completing these two steps, the new kernel is ready to be applied to circuit optimization and also usable by the hybrid kernel. The framework can also handle single objective algorithms using a vector of weights to convert a multi-objective problem in a single objective problem.