Abstract: Techniques are disclosed for a three-dimensional (3D) graphical rendering system, comprising: obtaining a first 3D graphical object, wherein the first 3D graphical object is associated with at least a first semi-transparent material, wherein the first material is associated with an adjustable density value and comprises at least a first plane (or 3D volume) with an adjustable position within a virtual environment; determining a transparency value based, at least in part, on the adjustable density value and a distance between the first plane and the first 3D graphical object (or a density of the 3D volume); and rendering, from a first viewpoint and using a first shader, at least a portion of the first 3D graphical object by applying the determined transparency value to the first material. Rendering the first 3D graphical object may further comprise blending between the first material and a second material according to the determined transparency value.

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.