Abstract:Circuit sizing (e.g. determining MOSFET channel widths and lengths which result in the most appropriate and robust circuit) is an optimization process. When it is completed, there always remains a dilemma, whether a better solution exists. With different starting points one can arrive at different local minima. A heuristic process, consisting of many optimization runs started from different initial points, is proposed. It tries to find another local minimum of the cost function in every run and thus reveals so… Show more
“…This constraint demands a reliable method to attain the transistor sizing by balancing all the desired parameters of the design. The use of metaheuristic optimization algorithms to determine the transistor sizes can be a solution to this problem (e.g., [ 239 , 240 ]). The minimization of the parameters can be accomplished by selecting one or more objective functions and considering the rest of the parameters as a constraint.…”
Section: Applications In Microelectronicsmentioning
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.
“…This constraint demands a reliable method to attain the transistor sizing by balancing all the desired parameters of the design. The use of metaheuristic optimization algorithms to determine the transistor sizes can be a solution to this problem (e.g., [ 239 , 240 ]). The minimization of the parameters can be accomplished by selecting one or more objective functions and considering the rest of the parameters as a constraint.…”
Section: Applications In Microelectronicsmentioning
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.
“…simplex size or maximal number of function evaluations), a special mechanism is applied to initialize a new simplex in the unexplored parts of the search space. Then the COMPLEX algorithm is restarted to find a new and hopefully better local minimum [9]. The process is repeated until the maximal number of CF evaluations is reached.…”
Section: Optimization Of Integrated Circuitsmentioning
This paper presents a new hybrid algorithm for global optimization of integrated circuits. The algorithm exploits the efficient search mechanism of differential evolution and good global search capabilities of simulated annealing, while avoiding their weaknesses. It is easy to implement and has only a few parameters. The performance of the algorithm is verified on seven real-world cases of integrated circuit design with promising results. The proposed algorithm was implemented in SPICE OPUS simulation and optimization tool and compared with a multistart version of the constrained simplex algorithm. It outperformed the latter in terms of the final solution quality and speed.
“…In [23], analog circuits with low voltage and low power were designed with the help of Hierarchical PSO (HPSO). It was seen that the HSPO converges faster in comparison to the conventional PSO and GA. Another heuristic optimization approach was presented in [24], [25] to design and optimize amplifier circuits.…”
This work introduces a Partition Bound Particle Swarm Optimization (PB-PSO) algorithm to enhance convergence rates in analog circuit optimization. Two new parameters, ζ 1 and ζ 2 , are incorporated to adaptively update particle velocities based on iteration numbers. The parameter ζ 1 depends on the nonlinear convergence factor (α) and the number of iterations, N . The results indicate that ζ 1 's optimal value occurs with α = 4. ζ 2 partitions iterations into two regions, aiding local and global search. The PB-PSO algorithm, implemented in Python, demonstrates higher convergence rates than existing methods, with successful designs verified through Cadence-Virtuoso circuit simulations. The proposed PB-PSO algorithm converges in 15 and 13 iterations for differential amplifier and two-stage op-amp respectively. For a case study of two-stage amplifier, it achieves a gain of 60.4 dB with a phase margin of 79.76 • , meeting input specifications within constraints. The figure of merit was then evaluated using the obtained parameters, which turns out to be 0.275 V −2 . INDEX TERMS Particle swarm optimization (PSO), analog circuit sizing, low power, operational amplifiers, constrained optimization.
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