A general formulation to develop EM-based polynomial surrogate models in frequency domain utilizing the multinomial theorem is presented in this paper. Our approach is especially suitable when the number of learning samples is very limited and no physics-based coarse model is available. We compare our methodology against other four surrogate modeling techniques: response surface modeling, support vector machines, generalized regression neural networks, and Kriging. Results confirm that our modeling approach has the best performance among these techniques when using a very small amount of learning base points on relatively small modeling regions. We illustrate our technique by developing a surrogate model for an SIW interconnect with transitions to microstrip lines, a dual band T-slot PIFA handset antenna, and a high-speed package interconnect. Examples are simulated on a commercially available 3D FEM simulator.
There is an increasingly higher number of mixedsignal circuits within microprocessors. A significant portion of them corresponds to high-speed input/output (HSIO) links. Postsilicon validation of HSIO links is critical to provide a release qualification decision. One of the major challenges in HSIO electrical validation is the physical layer (PHY) tuning process, where equalization techniques are typically used to cancel any undesired effect. Current industrial practices for PHY tuning in HSIO links are very time consuming since they require massive lab measurements. On the other hand, surrogate modeling techniques allow to develop an approximation of a system response within a design space of interest. In this paper, we analyze several surrogate modeling methods and design of experiments techniques to identify the best approach to efficiently optimize a receiver equalizer. We evaluate the models performance by comparing with actual measured responses on a real server HSIO link. We then perform a surrogate-based optimization on the best model to obtain the optimal PHY tuning settings of a HSIO link. Our methodology is validated by measuring the real functional eye diagram of the physical system using the optimal surrogate model solution.
To meet antenna design specifications under realistic conditions, electromagnetic coupling effects between the antenna and its environment must be considered. In this work, an efficient antenna design optimization methodology that considers the influence of the human head and main mobile handset components on the antenna performance is presented. The computational optimization time is dramatically reduced by exploiting a Broyden-based input space mapping (SM) algorithm. Both coarse and fine models required for the SM algorithm are based on the finite-element method and are implemented in the same simulator; simplifying the modeling process. However, our coarse model does not consider any object of the actual operational environment. In spite of that and other simplifications applied to the coarse model, the proposed optimization scheme is able to find a solution that meets the specifications in a realistic environment by performing an extremely small number of expensive fine model simulations. Our practical illustration opens up the feasibility of using this CAD methodology to optimize other RF devices that operate in close proximity to objects that affect its desired response, as it is the case for many wearable devices.
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