Artificial neural networks for fast and accurate EM-CAD of microwave circuits

Abstract

three-layer neural network with 15 neurons in the first layer and 11 neurons in the hidden layer reaches an E s 9.31 и rms 10 y3 , and it is able to synthesize filters which satisfy the Ž . requirements of the test set filters. In Figure 2 b , for the sake of brevity, we have only shown the FE analysis of three Ž . filters synthesized by the neural network continuous line Ž . and the original ones coming from the test set dotted line . As is apparent, the synthesized filters and the desired masks match well.It is worth pointing out that the reduced size of both learning and test sets can be allowed because of the extreme simplicity of the chosen filter. A larger learning set and a more sophisticated neural-network structure are required when dealing with more complex filtering devices; nevertheless, the features of this technique remain the same. IV. CONCLUSIONSIn this communication, a quick approach to microwave filter design is presented. This methodology has been applied to a low-order filtering device, giving good results and costing very little computational time in the neural-network learning process. It is worth mentioning that the versatility of the FE may be very useful to generate training sets for more involved microwave filtering devices. In these latter cases, the combined FErneural-network methodology becomes suitable for saving time during the microwave filter design procedure. Future work will be devoted to the use of this procedure in the case of more complex and different order waveguide and planar technology filtering structures as are required in practical applications.

A neural-network method for the analysis of multilayered shielded microwave circuits

“…NNs were also applied to microwave circuit optimization in [12] and [13]. After NNs proved their ability in microwave modeling and optimization, they were also used in the development of neural-based microwave CAD packages and library model tools [14], [15].…”

Robust training of microwave neural models

“…These neural models can be used in place of computationally intensive physics/EM models of active/passive devices to speed up microwave design. Neural network modeling techniques have been used for a wide variety of microwave applications such as microstrip interconnects [1,2], vias [3,4], spiral inductors [5], FET devices [1,6], and HBT devices [7]. Neural networks have also been used in circuit simulation and optimization [8], filter design [9], impedance matching [10], and synthesis [11].…”

Applications of artificial neural networks to RF and microwave measurements

“…In particular, researchers have successfully used ANNs to model microstrip vias [2], packaging and interconnects [3], spiral inductors [4], MESFET devices [5], CPW circuit components [6], effective dielectric constant of microstrip lines [7], and HBT amplifiers [8], to name just a few.…”