International Business Machines Corporation

Globalfoundries Inc.

Rouwaida N Kanj

Rajiv V Joshi

Sani Richard Nassif

Jente Benedict Kuang

Fadi Hikmat Gebara

Keunwoo Kim

Anne Elizabeth Gattiker

Anthony N Hylick

Carl John Radens

Hung Cai Ngo

Pranita Kerber

Jian Li

Jayakumaran Sivagnaname

Zhuo Li

Ying Liu

Jason D Hibbeler

Tong Li

Donald W Plass

Chad Allen Adams

Antonio R Pelella

Daniel Nelson Maynard

Noam Jungmann

Evanthia Papadopoulou

James W Dawson

Elazar Kachir

Sudesh Saroop

Sarah C Braasch

Ehud Nir

A mechanism is provided for reusing importance sampling for efficient cell failure rate estimation of process variations and other design considerations. First, the mechanism performs a search across circuit parameters to determine failures with respect to a set of performance variables. For a single failure region, the initial search may be a uniform sampling of the parameter space. Mixture importance sampling (MIS) efficiently may estimate the single failure region. The mechanism then finds a center of gravity for each metric and finds importance samples. Then, for each new origin corresponding to a process variation or other design consideration, the mechanism finds a suitable projection and recomputes new importance sampling (IS) ratios.

Statistical design with importance sampling reuse

Techniques for managing big data include tagging of documents and subsequent retrieval using per-subject dictionaries having entries with some entries specially designated as entities. An entity indicates that the term in the entry has special meaning, e.g., brands (trademarks/service marks), trade names, geographic identifiers or other classes of terms. A dictionary may include a non-entity entry for a term and one or more entity entries, for different entity types. The entries may also include subject-determining-power scores. The subject-determining-power scores provide an indication of the descriptive power of the term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different subject-determining-power scores in each of the dictionaries. The entity distinctions for a term can then be used in tagging documents and processing retrieval requests.

Document tagging and retrieval using entity specifiers

Techniques for managing big data include tagging of documents and subsequent retrieval using per-subject dictionaries having entries with subject-determining-power scores. The subject-determining-power scores provide an indication of the descriptive power of the term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different subject-determining-power scores in each of the dictionaries. A retrieval request for one or more documents containing search terms descriptive of the one or more documents can be processed identifying a set of candidate documents tagged with subjects and optional terms, and then applying subject-determining-power scores from the multiple dictionaries for the search term to determine a subject for the search term. The method then selects the one or more documents from the candidate documents according to the subject.

Document tagging and retrieval using per-subject dictionaries including subject-determining-power scores for entries

Selective importance sampling includes: first phase importance sampling of a plurality of data points to form a first subset of data points; determining a center of gravity of the first subset; determining, based upon the center of gravity, an orthogonal hyperplane; and a second phase importance sampling comprising: determining, for each point in the first subset, whether each point is above the hyperplane; and forming a second subset of data points, wherein the second subset is a subset of the first subset and wherein the second subset excludes each point of the first subset that has been determined to be above the hyperplane. The second subset also excludes all points within an ellipse below the hyperplane. Critical radial distances are determined from binary search or projection of first phase samples onto center of gravity direction as well as the maximal radius of the first subset around the center of gravity.

Selective importance sampling

Mechanisms are provided for modeling a plurality of devices of an integrated circuit design as a single statistically equivalent wide device. An integrated circuit design is analyzed to identify a portion of the integrated circuit design having the plurality of devices. For the plurality of devices, a statistical model of a single statistically equivalent wide device is generated which has a statistical distribution of at least one operating characteristic of the single statistically equivalent wide device that captures statistical operating characteristic distributions of individual devices in the plurality of devices. At least one statistical operating characteristic of the single statistically equivalent wide device is a complex non-linear function of the statistical operating characteristics of the individual devices. The integrated circuit design is modeled using the single statistically equivalent wide device.

Equivalent device statistical modeling for bitline leakage modeling

A method for analyzing circuits includes identifying one or more device zones in a full device structure. The device zones provide areas of interest to be analyzed. A partial device is generated that representatively includes the one or more device zones. Analytical meshes of the partial device are reduced by employing physical characteristics of the full device structure. The partial device is simulated, using a processor, to obtain device output information in the areas of interest that is representative of the full device structure. Systems are also disclosed.

Fet-bounding for fast TCAD-based variation modeling

Techniques for managing big data include retrieval using per-subject dictionaries having multiple levels of sub-classification hierarchy within the subject. Entries may include subject-determining-power (SDP) scores that provide an indication of the descriptive power of the entry term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different SDP scores in each of the dictionaries. A retrieval request for one or more documents containing search terms descriptive of the one or more documents can be processed by identifying a set of candidate documents tagged with subjects, i.e., identifiers of per-subject dictionaries having entries corresponding to a search term, then using affinity values to adjust the aggregate score for the terms in the dictionaries. Documents are then selected for best match to the subject based on the adjusted scores. Alternatively, the adjustment may be performed after selecting the documents by re-ordering them according to adjusted scores.

Document retrieval using internal dictionary-hierarchies to adjust per-subject match results

FET-bounding for fast TCAD-based variation modeling

In one embodiment, the invention is a method and apparatus for table-lookup-based models for yield analysis acceleration. One embodiment of a method for statistically evaluating a design of an integrated circuit includes simulating the integrated circuit and generating a lookup table for use in the simulating, the lookup table comprising one or more blocks that specify a device element for an associated bias voltage, wherein the generating comprises generating only those of the one or more blocks that specify the device element for a bias voltage that is required during the simulating.

Table-lookup-based models for yield analysis acceleration

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Round Rock, TX, United States of America

Rouwaida N Kanj