Abstract: A method for identifying specification window violations for a system is described. The method includes generating a sample set of input parameters. The system is simulated using the sample set to generate an output set. A mathematical model is best-fit to the output set. A set of desirability functions is defined to an out-of-spec condition. The model is then searched using the desirability functions to identify a worst-case data point. The worst-case data point can then be determined as either being within specification or out of specification.

Abstract: A method for identifying specification window violations for a system is described. The method includes generating a sample set of input parameters. The system is simulated using the sample set to generate an output set. A mathematical model is best-fit to the output set. A set of desirability functions is defined to an out-of-spec condition. The model is then searched using the desirability functions to identify a worst-case data point. The worst-case data point can then be determined as either being within specification or out of specification.

Abstract: A method for identifying specification window violations for a system is described. The method includes generating a sample set of input parameters. The system is simulated using the sample set to generate an output set. A mathematical model is best-fit to the output set. A set of desirability functions is defined to an out-of-spec condition. The model is then searched using the desirability functions to identify a worst-case data point. The worst-case data point can then be determined as either being within specification or out of specification.

Abstract: A system and method for deriving semiconductor manufacturing process corners using surrogate simulations is disclosed. The method may be used to determine individual performance metric yields, the number of out-of-specification conditions for a given number of simulation samples, and a total yield prediction for simultaneous multi-variable conditions. A surrogate simulation model, such as a Response Surface Model, may be generated from circuit simulation data or parametric data measurements and may be executed using a large number of multi-variable sample points to determine process corners defining yield limits for a device. The model may also be used to simulate process shifts and exaggerated input ranges for critical device parameters. In some embodiments, the derived process corners may better represent physically possible worst-case process corners than traditional general-purpose process corners, and may address differences in process sensitivities for individual circuits of the device.

Abstract: A system and method for deriving semiconductor manufacturing process corners using surrogate simulations is disclosed. The method may be used to determine individual performance metric yields, the number of out-of-specification conditions for a given number of simulation samples, and a total yield prediction for simultaneous multi-variable conditions. A surrogate simulation model, such as a Response Surface Model, may be generated from circuit simulation data or parametric data measurements and may be executed using a large number of multi-variable sample points to determine process corners defining yield limits for a device. The model may also be used to simulate process shifts and exaggerated input ranges for critical device parameters. In some embodiments, the derived process corners may better represent physically possible worst-case process corners than traditional general-purpose process corners, and may address differences in process sensitivities for individual circuits of the device.

Abstract: A method and computer aided system for predicting the reliability of oxide-nitride-oxide (ONO) based non-volatile memory. ONO memory devices may be programmed. Margin voltages may be recorded initially, and during baking at 100 degrees C. and 300 degrees C. From this data, constants and activation energy may be determined through a first formula. Frenkel-Poole activation energy may be determined. Through the use of a second formula, decay time of the information stored in the ONO memory may be predicted from the activation energy. The first formula may also be used to predict the decay time. The two decay time predictions may be compared to establish confidence. In this manner, data retention of an ONO memory may be reliably predicted.