Advanced Micro Devices Corporation

Siu-May Ho

Yeng-Kaung Peng

Ying Shiau

Yung-Tao Lin

Zhi-Min Ling

Chern-Jiann Lee

Thao H Vo

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. The model can be improved by self-learning using additional test values. The model can also be used to identify parameters which result in low performance and improve devices being fabricated by adjusting the corresponding process parameters.

Method for predicting performance of microelectronic device based on

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. The report is also stored in a pattern database to serve as a library for future reference of previously recognized fault patterns, thereby to bypass the need to perform a failure analysis for matching fault patterns.

Method and apparatus for pattern recognition of wafer test bins

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. The yield management station determines at least one of a number of killer defects for the devices in each of the layers or a defect sensitive area index for each of the layers using the first and second information. The yield management station also determines a priority for analyzing each of the at least two layers responsive to at least one of the number of killer defects and the defect sensitive area index for each of the layers.

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San Jose, CA, United States of America

Siu-May Ho