Intel Corporation

Nvidia Corporation

Vivek K Singh

Bikram Baidya

Bin Hu

Yan A Borodovsky

John A Swanson

Kumara Sastry

Allan Gu

Abde Ali Hunaid Kagalwalla

Sasikanth Manipatruni

Brian S Doyle

Shawna M Liff

Peng Liu

Prasad N Atkar

Saumyadip Mukhopadhyay

Sungwon Kim

Francisco A Leon

Shem Ogadhoh

Seongtae Jeong

David Fryer

Omkar S Dandekar

John Ernst Bjorkholm

Raguraman Venkatesan

Kevin J Hooker

Prasad Narendra Atkar

Hale Erten

Dmitri E Nikonov

Chulwoo Oh

Paul Davids

Michael C Mayberry

Nikolay Suetin

Kenny K Toh

Bikram Baidva

Aswin Sreedhar

Alex A Granovsky

Daniel L Stahlke

Srinivas B Bollepalli

Victor P Bashurin

Kritika Upreti

Yuri D Bogunenko

Thanh N Phung

Kshitij Auluck

Md Ashraful Alam

Helen F Parks

Mengfei Yang-Flint

Kasyap Thottasserymana Vasudevan

I-Tzu Chen

Selim Dogru

Kumara Narasimha Sastry Kunigal

John Swanson

Lillian Chang

Manuj Swaroop

Mehmet E Yavuz

In order to determine contour edges within a provided image, a plurality of image cells (e.g., groupings of pixels) are created within the image. For each image cell, a numerical value for each of the pixels is compared to a predetermined threshold value to determine comparison values for each pixel. A total numerical value for each image cell may then be determined utilizing the comparison values and numerical values for each pixel within each image cell. An associated contour cell (indicating present contour edges) is then determined for each image cell by comparing the total numerical value for the image cell to a contour cell index. These operations may be performed in parallel by a graphics processing unit (GPU) for each image cell, which may improve a performance of contour edge determination for the image. The stitching of contour edges may also be performed using the GPU, which may provide additional performance improvements for image contour extraction.

Determining contour edges for an image

Embodiments of the present disclosure relate to parallel mask rule checking on evolving mask shapes in optical proximity correction (OPC) flows for integrated circuit designs. Systems and methods are disclosed that perform mask (manufacturing) rule checks (MRC) in parallel, sharing information to maintain symmetry when violations are corrected. In an embodiment the shared information is also used to minimize changes to the geometric area of proposed mask shapes resulting from the OPC. In contrast to conventional systems, MRC is performed for multiple edges in parallel, sharing information between the different edges to encourage symmetry. In an embodiment, all edges may be adjusted in parallel to reduce mask-edge traversal bias.

Parallel mask rule checking on evolving mask shapes in optical proximity correction flows

A method comprising identifying a set of target features for a plurality of data instances of an input data collection; determining feature values for the set of target features for the plurality of data instances; identifying a plurality of outlier data instances based on the determined feature values; identifying a plurality of noisy data instances from the outlier data instances based on feature values of the plurality of noisy data instances, wherein a noisy data instance is identified based on a determination that noise is present in noisy data instance; and providing an indication of the plurality of noisy data instances.

Automatic elimination of noise for big data analytics

A method comprising identifying a plurality of non-overlapping coarse domains of a region of interest; selecting a subset of the plurality of coarse domains based on a plurality of first diversity metrics determined for the plurality of coarse domains; identifying a plurality of non-overlapping fine domains of the region of interest, wherein each of the fine domains is a portion of one of the coarse domains of the selected subset of the plurality of coarse domains; selecting a subset of the plurality of fine domains based on a plurality of second diversity metrics determined for the plurality of coarse domains; and providing an indication of the selected subset of the plurality of fine domains.

Hierarchical graph-based domain selection algorithm to improve diversity

A method includes identifying a first geometric pattern that failed a design rule check, identifying a second geometric pattern that passed the design rule check, morphing the first geometric pattern based on the second geometric pattern to generate a morphed geometric pattern, wherein the morphed geometric pattern passes the design rule check, and replacing the first geometric pattern with the morphed geometric pattern.

Image morphing to meet desired constraints in geometric patterns

Images are accessed representing a status in a fabrication of a semiconductor chip corresponding to a particular stage in the fabrication. Distortion is removed from the images and actual features of the semiconductor chip are extracted from the images. Synthesized ideal features of the semiconductor chip associated with completion of the particular stage in the fabrication are determined from the one or more images. The actual features are compared to the ideal features to determine whether anomalies associated with the particular stage exist in the semiconductor chip.

Unsupervised clustering to identify anomalies

A method includes, for each data object of a plurality of data objects, performing a measurement on a plurality of instances of the data object to generate a plurality of measurement values for the data object, and generating a distribution of the measurement values for the data object. The method further includes generating an aggregate distribution based on each of the distributions of the measurement values generated for the data objects, and scoring a first data object of the plurality of data objects based on the distribution of the measurement values for the first data object and the aggregate distribution.

Ranking of objects with noisy measurements

A method comprising determining a binary classification value for each of a plurality of data instances based on a first threshold value assigned to each of the plurality of data instances; applying at least one clustering model to a first subset of the plurality of data instances to identify one or more dominant clusters of data instances; determining a second threshold value to assign to a second plurality of data instances that are included within the one or more dominant clusters of data instances; and redetermining a binary classification value for each of the plurality of data instances based on the second threshold value assigned to the second plurality of data instances and the first threshold value, wherein the first threshold value is assigned to at least a portion of data instances of the plurality of data instances that are not included in the second plurality of data instances.

Iterative supervised identification of non-dominant clusters

A search engine receives data describing reference geometry and generates a hash based on the reference geometry. A reference bloom filter is generated for the reference geometry based on the hash. The search engine performs a search to determine whether instances of the reference geometry are present in an integrated circuit (IC) layout. The search includes comparing the reference bloom filter with each one of a plurality of bloom filters corresponding to a plurality of subdomains of the IC layout. Based on results of the comparison, one or more subdomains of interest are identified and searched to determine whether the particular reference geometry is present in the subdomain.

Fast pattern matching

A semantic pattern extraction system can distill tremendous amounts of silicon wafer manufacturing data to generate a small set of simple sentences (semantic patterns) describing physical design geometries that may explain manufacturing defects. The system can analyze many SEM images for manufacturing defects in areas of interest on a wafer. A tagged continuous itemset is generated from the images, with items comprising physical design feature values corresponding to the areas of interest and tagged with the presence or absence of a manufacturing defect. Entropy-based discretization converts the continuous itemset into a discretized one. Frequent set mining identifies a set of candidate semantic patterns from the discretized itemset. Candidate semantic patterns are reduced using reduction techniques and are scored. A ranked list of final semantic patterns is presented to a user. The final semantic patterns can be used to improve a manufacturing process.

Semantic pattern extraction from continuous itemsets

An improved random forest model is provided, which has been trained based on silicon data generated from tests of previously fabricated chips. An input is provided to the random forest model, the input including a feature set of a pattern within a particular chip layout, the feature set identifying geometric attributes of polygonal elements within the pattern. A result is generated by the random forest model based on the input, where the result identifies a predicted attribute of the pattern based on the silicon data, and the result is generated based at least in part on determining, within the random forest model, that geometric attributes of the pattern were included in the previously fabricated chips, where the previously fabricated chips have chip layouts are different from the particular chip layout.

Random forest model for prediction of chip layout attributes

A machine readable storage medium, a method and an apparatus. The method comprises selecting a candidate set of parameters from a plurality of available parameters comprising variables that affect an outcome of a lithography process; performing a set of optimizations wherein each optimization of the set of optimizations is subject to a plurality of objectives and tolerances and a set of constraints, wherein performance of said each optimization comprises: modifying values of at least a portion of the candidate set of parameters to derive a predicted outcome for said each optimization; and determining whether a difference between the predicted outcome and an intended outcome is within an error threshold; and if the difference exceeds the error threshold, perform a subsequent optimization, and otherwise generate an input file including modified values, corresponding to a last one of the set of optimizations, for the at least a portion of the candidate set of parameters.

Adaptive algorithm to generate optical proximity correction lithographic recipe

Flexible electronically functional fabrics are described that allow for the placement of electronic functionality in flexible substrates such as traditional fabrics. The fabrics can be made using flexible electronically functional fibers or a combination of electronically functional fibers and textile fibers. Electronic devices can be incorporated into the fabric to give it full computing capabilities.

Electrically functional fabric for flexible electronics

Flexible electronically functional fibers are described that allow for the placement of electronic functionality in traditional fabrics. The fibers can be interwoven with natural fibers to produce electrically functional fabrics and devices that can retain their original appearance.

Flexible embedded interconnects

A charge storage fiber is described. In an embodiment, the charge storage fiber includes a flexible electrically conducting fiber, a dielectric coating on the flexible electrically conducting fiber, and a metal coating on the dielectric coating. In an embodiment, the charge storage fiber is attached to a textile-based product.

Structure to make supercapacitor

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Portland, OR, United States of America

Vivek K Singh