Rapid ULSI Interconnect Reliability Analysis Using Neural Networks

Neural network modeling method is introduced for analyzing ultralarge scale integration (ULSI) interconnect reliability for the first time. By training the simulation data from ANSYS (a finite-element tool), a neural network model is developed, where the prediction of ULSI interconnect reliability can be more effectively done. The proposed technique is useful for integrated circuit design since it can produce a database of interconnect layouts with reliability comparison for a given circuit. From the database, we can know the relative reliability of interconnect layout at any given temperature or current rapidly. Through this proposed technique, we can also derive the allowable temperature and current range of a circuit to ensure given reliability criteria.