Aaron Barker

US Patent:

7716023, May 11, 2010

Filed:

Feb 13, 2007

Appl. No.:

11/674572

Inventors:

Aaron J. Barker - Broomfield CO, US

Assignee:

Oracle America, Inc. - Redwood Shores CA

International Classification:

G06F 17/50
G06F 9/00

US Classification:

703 2, 703 14, 716 2, 716 19, 382103

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.

US Patent:

8271256, Sep 18, 2012

Filed:

Aug 13, 2009

Appl. No.:

12/540890

Inventors:

Ebrahim Khalily - Los Altos CA, US
Aaron J. Barker - Broomfield CO, US
Alexandru N. Ardelea - Austin TX, US

Assignee:

Oracle America, Inc. - Redwood City CA

International Classification:

G06F 17/50

US Classification:

703 14, 716132

Abstract:

A method of optimizing MOSFET device production which includes defining key independent parameters, formulating those key independent parameters into a canonical variational form, calculating theoretical extracted parameters using at least one of key independent parameters in canonical variational form, physics-based analytical models, or corner models. The method also includes calculating simulated characteristics of a device using the key independent parameters and extracting target data parameters based on at least one of measured data and predicted data, comparing the simulated characteristics to the target data parameters, and modifying the theoretical extracted parameters or key independent parameters in canonical form as a result of the comparison. Then, calculating and outputting the simulated characteristics based on the modified theoretical extracted parameters and the modified key independent parameters in canonical form.

US Patent:

20220414305, Dec 29, 2022

Filed:

Jun 29, 2021

Appl. No.:

17/362417

Inventors:

- Mountain View CA, US
Aaron John Barker - Broomfield CO, US

International Classification:

G06F 30/3308

Abstract:

Samples of metrics measured on physical devices are selected from a larger number of samples. The samples are selected based on the distributions of the measured metrics. A set of model instances are constructed that correspond to the selected set of samples. The model instances have parameters, which are set such that simulation of the model instances using the parameters predicts metrics that match the measured metrics from the set of samples. The principal components of the variances of the parameters is calculated. Non-linear models are fitted to the parameter variances as a function of the principal components. Statistical variations of the principal components are applied to the non-linear models to yield statistical variations in the parameters; and these are applied to simulations of model instances to yield statistical variations of a property of the device being simulated.

US Patent:

20140258950, Sep 11, 2014

Filed:

Mar 6, 2013

Appl. No.:

13/786927

Inventors:

- Redwoon City CA, US
Aaron J. Barker - Broomfield CO, US

Assignee:

ORACLE INTERNATIONAL CORPORATION - Redwood City CA

International Classification:

G06F 17/50

US Classification:

716112

Abstract:

Systems and methods are described for simultaneously deriving an effective x-sigma corner for multiple, different circuit and/or process metrics for a semiconductor device. The result is an effective sigma that is representative of design intent. Some implementations account for covariance, and use joint probability as the criteria for the effective x-sigma corner (e.g., as opposed to a unique sigma level of each individual metric). Analysis results for each metric can be transformed to metric distributions in a common distribution framework, and a correlation matrix can be calculated. The transformed metric distributions can be input to a joint probability distribution set to achieve a target joint sigma level. The joint probability distribution and correlation matrix values can be used to back-calculate scaled x-sigma corners for each metric distribution. Simulation of the device can be performed at one or more of the scaled x-sigma corners.