Abstract: Electrical parameter testing and performance testing are performed on a plurality of microelectronic devices to obtain parametric values and performance values respectively. The parametric values are applied as inputs to a computer program such as a back propagation neural network engine which generates a performance prediction model by self-learning that implements a function relating the performance values to the parametric values. The model is used to predict the performance of devices being fabricated by performing electrical parameter testing on these devices and applying the resulting parametric values to the model as inputs to produce predicted performance values as outputs. The model can be configured to produce predicted performance values as percentages of devices having speed or other parameters in predetermined respective ranges. The model can be further configured to produce predicted performance values as percentages of devices having different types of defects.

Abstract: Defects in integrated circuit wafers (10) are often difficult to diagnose, because patterned wafer inspections can only be done after certain wafer processing steps. Defect simulation is used to understand the relation between defects in the wafer (10) and the resulting wafer profiles. Defects such as particles (50) and bubbles (22) in the photoresist (28), for example, translate into a wide variety of defective profiles. Knowledge of the relation between defects and the defect profiles can assist in yield improvement efforts, since defects may be diagnosed by comparing simulated and observed defect profiles. From the simulated defect profiles, methods can be adapted to fix or correct observed defects.

Abstract: Methods and apparatus are disclosed for testing integrated circuits at the wafer level and for integrating test results, calculation of lifetimes and generation of trend charts in a common database following testing. A wafer tester controller is supplemented with additional hardware and software to avoid data transfer errors and facilitate processing and storage of test results. The data base is available over a network to all areas of an organization.

Abstract: An automated system and procedure processes wafer test bin data of semiconductor wafers to formulate a fault pattern at statistically significant levels. A processor such as a neural engine or neural network collects wafer test bin results to generate a N/N wafer map to be correlated with wafer maps produced from a wafer electrical test, a wafer level reliability test, and an in-line defect analysis. A N/N wafer map generated by the processor is cross-checked with a wafer map generated from another semiconductor tester to formulate possible overlap fault patterns. The confirmed fault patterns are further analyzed by performing failure analysis to find the root cause of fault patterns. A report containing fault patterns and the root cause for fault patterns is sent back to a fab for making adjustment to the fabrication process to increase the overall yield of the future batch of semiconductor wafers.

Abstract: A real-time in-line defect disposition and yield forecasting system for a semiconductor wafer having layer containing devices includes an in-line fabrication inspection tool, a design review station, and a yield management station. The in-line fabrication inspection tool inspects at least two layers of the semiconductor wafer and produces first information including particle size, particle location and number of particles introduced therein for each of these layers. The design review station inspects the layers of the semiconductor wafer and produces second information including layouts of each of the layers. The yield management station is operatively connected to the in-line fabrication inspection tool and to the design review station. The yield management station retrieves the first information and the second information from the in-line fabrication inspection tool and from the design review station.

Abstract: A method of analyzing a failure of a sample, such as a wafer or a package unit made from a die sliced from the wafer, uses a computer aided design (CAD) tool in conjunction with a dual beam scanner and reverse engineering to improve the yield of the product. The computer aided design tool provides navigation to a location of interest over a layout of a wafer sample which has failed a test. The location of interest of the sample is then inspected using the dual beam scanner. The inspection may be made with either a focused ion beam scan or with a scanning electron microscope scan to provide different types of scan images and information. After inspection, a reverse engineering process (stripping back) is performed on the sample and the sample is inspected again to determine the cause of the failure of the sample. Once the cause of the failure is determined, the manufacturing process can be changed to improve the yield of the wafers.