Abstract: A transistor device may include a semiconductor structure including a channel layer and a barrier layer on the channel layer, wherein the barrier layer has a higher bandgap than the channel layer; a source contact and a drain contact on the barrier layer; a gate contact on the semiconductor structure between the source contact and the drain contact, the gate contact including a drain-side wing portion extending from a central portion of the gate contact; and a field plate on the semiconductor structure between the gate contact and the drain contact and laterally offset from the gate contact by a distance. The field plate may include a first wing portion extending from a central portion of the field plate.

Abstract: A method and system to estimate failure rates in designs. N Monte Carlo samples are drawn from the random distribution that describes process variation in the design. A subset of these samples is selected, and that subset of Ninit samples are simulated (with a circuit simulator) to measure a performance value for each sample. A model is constructed, using the values of the Ninit process points as training inputs, and the corresponding Ninit performance values as training outputs. The candidate Monte Carlo samples are from the N Monte Carlo samples that have not yet been simulated. Each candidate is simulated on the model to get predicted performance values, and the samples are ordered in ascending (or descending) order of the predicted performance values. Simulation of candidates samples is then begun, in that order. The sampling and simulation will stops once there is sufficient confidence that all failures are found.

Abstract: A method and system to estimate failure rates in designs. N Monte Carlo samples are drawn from the random distribution that describes process variation in the design. A subset of these samples is selected, and that subset of Ninit samples are simulated (with a circuit simulator) to measure a performance value for each sample. A model is constructed, using the values of the Ninit process points as training inputs, and the corresponding Ninit performance values as training outputs. The candidate Monte Carlo samples are from the N Monte Carlo samples that have not yet been simulated. Each candidate is simulated on the model to get predicted performance values, and the samples are ordered in ascending (or descending) order of the predicted performance values. Simulation of candidates samples is then begun, in that order. The sampling and simulation will stops once there is sufficient confidence that all failures are found.