Abslruct-The validity of extracted microwave device models is critically dependent on the completeness, accuracy, and appropriateness of the starting device characterization data. In this letter we will present a novel technique for determining the S-parameters of a device under isothermal (i.e., no heating) operation. Additionally, this technique can be applied to determining the CW S-parameters under more extreme (e.g., forward biashreakdown) operation. By pulse-biasing the device from the "OFF" to the "ON" state, while performing standard S-parameter measurements, resultant data is found to be characteristic of the weighted (by duty factor) scalar sum of the devices "ON"-state and "OFF"-state S-parameter@). We will show how these measurements can then be used to interpret the devices isothermal CW S-parameters.

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.

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSORING/MONITORING AGENCY ACRONYM(S)AFRL ABSTRACTThis paper addresses experimental RF performance evaluation, and electrical parameter extraction of different size ferroelectric varactor shunt switches. The ferroelectric varactor shunt switch operation is based on nonlinear dielectric tunability of a Ba 0.6 Sr 0.4 TiO 3 (BST) thinfilm sandwiched between two metal layers in the parallel plate configuration. Coplanar waveguide implementation of the varactor shunt switch results in a high speed RF switch, with a simple two-metal layer Si MMIC compatible process on high resistivity Si substrates. Experimental RF performance of the switches show low insertion loss for smaller area devices, with good isolation for larger area devices. To optimize the device design, RF performance of multiple devices were tested, and electrical parameters were extracted. The capacitance of the varactor shunt switches tested were tunable more than 4:1 for bias voltages below 12 V. The switching speed of the devices tested was approximately 43 ns based on the step response measurements. performance of multiple devices were tested, and electrical parameters were extracted. The capacitance of the varactor shunt switches tested were tunable more than 4:1 for bias voltages below 12 V. The switching speed of the devices tested was approximately 43 ns based on the step response measurements. SUBJECT TERMS

A unique approach for applying neurocomputing technology for accurate CAD of microwave circuits is described. In our proposed method, a multilayer perceptron neural network (MLPNN) is trained to predict the scattering parameters of MMIC passive elements based on the element's physical dimensions. The sparameters were obtained by performing a fullwave electromagnetic (EM) analysis of these elements. An X-band MLPNN spiral inductor model is developed. The MLPNN computed sparameter values are in excellent agreement with those obtained from E M simulations with correlations greater than 0.99 for all modeled parameters.