Method for robust statistical semiconductor device modeling
AssigneeGlobalfoundries Inc.
InventorsVineet Wason, Jung-Suk Goo, Zhi-Yuan Wu, Ciby T. Thuruthiyil
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Abstract
According to one exemplary embodiment, a method for robust statistical semiconductor device modeling includes building a semiconductor device model using at least one new device parameter variation, constructing a variation library for the semiconductor device model, and verifying the variation library against measured data from physical semiconductor devices. The variation library is constructed by determining variations of the at least one new device parameter variation and standard device parameters as functions of, for example. sizes and locations of semiconductor devices on semiconductor dies.
Claims17
A method for execution in a processor of a computer, said method for robust statistical semiconductor device modeling, said method comprising the steps of:
- building a semiconductor device model using at least one standard device parameter and at least one new device parameter variation, wherein said at least one new device parameter variation is obtained, at least in part, from a data set acquired by measurement, and is related to a work function variation, a line edge roughness, or a random dopant fluctuation;
- constructing a variation library for said semiconductor device model, wherein said variation library is constructed based on changes in at least one new device parameter;
- running simulations using said semiconductor device model and said variation library, wherein said variation library is used to adjust parameters of said semiconductor device model;
- verifying said variation library based on a comparison between results of said simulations and measured data from a physical semiconductor device;
- fine-tuning said variation library based on said comparison.
A computer programmed with code to execute a method for robust statistical semiconductor device modeling, said computer configured to perform the steps of:
- building a semiconductor device model using at least one standard device parameter and at least one new device parameter variation, wherein said at least one new device parameter variation is obtained, at least in part, from a data set acquired by measurement, and is related to a work function variation, a line edge roughness, or a random dopant fluctuation;
- constructing a variation library for said semiconductor device model, wherein said variation library is constructed based on changes in at least one new device parameter;
- running simulations using said semiconductor device model and said variation library, wherein said variation library is used to adjust parameters of said semiconductor device model;
- verifying said variation library based on a comparison between results of said simulations and measured data from a physical semiconductor device;
- fine-tuning said variation library based on said comparison.
A system for robust statistical semiconductor device modeling, said system obtaining a set of measured data from a wafer under test, said wafer under test including a physical semiconductor device, said set of measured data being utilized by a processor of a computer of said system to implement a method for robust statistical semiconductor device modeling, said computer configured to perform the steps of:
- building a semiconductor device model using at least one standard device parameter and at least one new device parameter variation, wherein said at least one new device parameter variation is obtained, at least in part, from a data set acquired by measurement, and is related to a work function variation, a line edge roughness, or a random dopant fluctuation;
- constructing a variation library for said semiconductor device model, wherein said variation library is constructed based on changes in at least one new device parameter;
- running simulations using said semiconductor device model and said variation library, wherein said variation library is used to adjust parameters of said semiconductor device model;
- verifying said variation library based on a comparison between results of said simulations and said set of measured data from said physical semiconductor device;
- fine-tuning said variation library based on said comparison.
Dependencies are inferred from the claim text.