By Michael Eick, Helmut Graeb (auth.), Mourad Fakhfakh, Esteban Tlelo-Cuautle, Rafael Castro-Lopez (eds.)
Despite the truth that within the electronic area, designers can take complete advantages of IPs and layout automation instruments to synthesize and layout very complicated structures, the analog designers’ activity continues to be regarded as a ‘handcraft’, bulky and extremely time eating strategy. therefore, great efforts are being deployed to strengthen new layout methodologies within the analog/RF and mixed-signal domain names.
This booklet collects sixteen state of the art contributions dedicated to the subject of systematic layout of analog, RF and combined sign circuits. Divided within the elements Methodologies and strategies contemporary theories, synthesis suggestions and layout methodologies, in addition to new sizing techniques within the box of sturdy analog and combined sign layout automation are offered for researchers and R/D engineers.
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Extra info for Analog/RF and Mixed-Signal Circuit Systematic Design
2 Handling Difficult to Predict Data Sets After lowering the original plain optimization problem with 10-20 design variables to a hierarchical optimization problem with 4-5 variables, the GP model predictions are more difficult. Through experiments, we found that using MMLDE, a satisfactory solution often needs many iterations, and each iteration is computationally expensive. The reason is that because of the complexity of the prediction problem, the number of wrong selections using the EI prescreening increases a lot.
In evolutionary optimization, the new population is generated according to the information of the previous population by evolution operators. If the amount of beneficial information in the previous population is less, generating promising candidates is more difficult. Our idea to solve this problem is to artificially increase the amount of beneficial information by constructing a new population for evolution. In each iteration, we rank all the candidates in the original population and select the top 75% candidates to enter the new population for evolution.
44 B. Liu and G. Gielen Consequently, the percentage of beneficial information in the consecutive populations increases slowly. In evolutionary optimization, the new population is generated according to the information of the previous population by evolution operators. If the amount of beneficial information in the previous population is less, generating promising candidates is more difficult. Our idea to solve this problem is to artificially increase the amount of beneficial information by constructing a new population for evolution.
Analog/RF and Mixed-Signal Circuit Systematic Design by Michael Eick, Helmut Graeb (auth.), Mourad Fakhfakh, Esteban Tlelo-Cuautle, Rafael Castro-Lopez (eds.)