Download Analog/RF and Mixed-Signal Circuit Systematic Design by Michael Eick, Helmut Graeb (auth.), Mourad Fakhfakh, Esteban PDF

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 merits of IPs and layout automation instruments to synthesize and layout very complicated structures, the analog designers’ activity remains to be regarded as a ‘handcraft’, bulky and extremely time eating technique. therefore, super 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 components Methodologies and strategies fresh theories, synthesis ideas and layout methodologies, in addition to new sizing techniques within the box of sturdy analog and combined sign layout automation are awarded for researchers and R/D engineers.

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The new exact point will be used to update the surrogate model. We iteratively repeat this process until the termination condition is met. An important advantage of MMLDE is the use of the on-line surrogate model, which is constructed based on the available data in the optimization process. The promising solutions are selected meanwhile and guide further candidate solution generation. The advantages on efficiency and reliability of on-line surrogate model-based optimization compared with off-line surrogate model-based optimization [19] are described and compared in [6].

The expected improvement (EI) prescreening method plays a key role in MMLDE and EMLDE, and is introduced in the next subsection. 2 Expected Improvement Prescreening This subsection introduces the expected improvement (EI) prescreening associated with Gaussian Process surrogate modeling. Before introducing EI, a first note is that the prescreening methods (besides directly using the predicted values) are only suitable for GP-based surrogate modeling, since the prescreening methods are also based on the Gaussian stochastic process.

Hence, if we replace some low-quality candidates, the diversity does not decrease too much. Based on the above ideas, the ABGPDE algorithm is constructed. 3. 3). The inputs are the transistor parameters, transmission line parameters and DC voltages. The used transformer or inductor and its EM simulation result are provided from the outer loop. The output is the performance of one stage of the RF amplifier with optimized transistors, transmission lines and biasing voltages. The construction of SBDE is quite simple.

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