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’ job remains to be regarded as a ‘handcraft’, bulky and extremely time eating strategy. therefore, great efforts are being deployed to increase new layout methodologies within the analog/RF and mixed-signal domain names.
This e-book 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 methods fresh theories, synthesis ideas and layout methodologies, in addition to new sizing methods within the box of strong analog and combined sign layout automation are awarded for researchers and R/D engineers.
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Additional info for Analog/RF and Mixed-Signal Circuit Systematic Design
G. power gain), the computationally expensive parts are: the parasitic extraction of active devices (transistors), the EM simulation of the long transmission lines and the EM simulation of transformers and inductors. When the performance optimization is based on linear analysis, usually the most critical problem is the impedance matching, and the transistors often have clear design rules. For example, a typical method is to use the minimum transistor length and a fixed width, while only the number of fingers is changed.
It can be seen that EMLDE decreases the number of expensive EM simulations carried out by about 80 times. Although there are much more circuit simulations in EMLDE, the linear circuit simulation is cheap. We can also conclude that the more complex the key passive components, which need more EM simulation time, the higher the advantage of EMLDE. 7 Conclusion This chapter has reported the efficient high-frequency synthesis methods for the synthesis of integrated passive components (with a method called memetic machine learning-based differential evolution (MMLDE)) as well as for the synthesis of mm-wave-frequency linear amplifiers (with a method called Efficient Machine Learning-based Differential Evolution (EMLDE)).
Step 4: Train the selected surrogate model according to the available samples (population). 4)). 6)). Step 7: According to the model selected in Step 3, use the EI or the predicted value to select the individual with the possible best potential and perform the EM simulation to it. Step 8: Update the population by adding the point from step 7 and its performance. Update the best solution obtained so far. Update other parameters. Go back to Step 2. 4 Experimental Results of MMLDE In this section, one example is shown to verify MMLDE.