International Business Machines Corporation

Globalfoundries Inc.

Sani Richard Nassif

Kanak Behari Agarwal

Rajiv V Joshi

Rouwaida N Kanj

Ying Liu

Emrah Acar

Jente Benedict Kuang

Anne Elizabeth Gattiker

Xiaokang Shi

Chin Ngai Sze

Damir Anthony Jamsek

Jerry D Hayes

Hung Cai Ngo

Wael R El-Essawy

Chandler Todd McDowell

Alexandre Peixoto Ferreira

Carl John Radens

Lars W Liebmann

Fadi Hikmat Gebara

Shayak Banerjee

Jeremy Daniel Schaub

Thomas Walter Keller

Jayakumaran Sivagnaname

John Patrick Keane

Tom Osiecki

Zhuo Li

Anirudh Devgan

Haihua Su

Rani S Abou Ghaida

James F Plusquellic

David William Boerstler

Charles Jay Alpert

Karthick Rajamani

Jason D Hibbeler

William Evan Speight

Chad Allen Adams

Juan C Rubio

Wendy Ann Belluomini

Daniel Nelson Maynard

Ronald Dennis Rose

Thomas W Keller, Jr

Ying Zhou

Tomasz J Nowicki

Peter Feldmann

Rahul M Rao

Evanthia Papadopoulou

Bhavna Agrawal

Grzegorz Michal Swirszcz

Thomas Henry Osiecki

Sarah C Braasch

Ravishankar Arunachalam

Dhruva J Acharyya

Rousaida N Kanj

Moyra K McManus

Matthew J Sullivan

Min-Yu Tsai

Yilin Zhang

Chenggang Xu

Ann Elizabeth Gattiker

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

A method and system are provided for converting clinical criteria constraints for a target structure of radiation therapy into a second set of constraints. The method includes selecting, by a hardware processor, a plurality of clusters of voxels in the target structure based on pre-specified criteria. The method further includes assigning, by the hardware processor, each of the plurality of clusters of voxels a respective constraint that specifies one or more bounds on a radiation dose applied to each voxel in a corresponding one of the plurality of clusters of voxels. The method also includes storing, in a memory device, the respective constraint for each of the plurality of clusters of voxels.

Translating different clinical protocols for particle therapy into a set of constraints

A system, method and computer program product for sorting Integrated Circuits (chips), particularly microprocessor chips, and particularly that predicts chip performance or power for sorting purposes. The system and method described herein uses a combination of performance-predicting parameters that are measured early in the process, and applies a unique method to project where the part, e.g., microprocessor IC, will eventually be sorted. Sorting includes classifying the IC product to a subset of a family of products with the product satisfying certain performance characteristics or specifications, in the early stages of manufacturing, e.g., before the end product is fully fabricated.

Power and performance sorting of microprocessors from first interconnect layer to wafer final test

Simulating particle beam interactions includes identifying a set of n functions F, F, . . . , Fcorresponding to a plurality of different physical aspects of a particle beam, performing simulations of each Fusing a full physics model, selecting for each Fa distribution function fthat models relevant behavior and reducing computation of the full physics model for each Fby replacing Fwith a distribution function f. The computation reduction includes comparing a set of simulations wherein each freplaces its respective Fto determine if relevant behavior is accurately modeled and selecting one of for Ffor each n, for a Monte Carlo simulation based on a runtime and accuracy criteria.

Fast and accurate proton therapy dose calculations

A method and system are provided. The method includes condensing, by a processor, an original voxel-beamlet matrix stored in a memory device into a reduced dataset for proton beam simulation and therapy. The original voxel-beamlet matrix has a row for each of a plurality of voxels in a three-dimensional patient volume and a column for each of a plurality of radiation beamlets. The condensing step includes determining protobeams to be extracted from the original voxel-beamlet matrix. The protobeams are columns (i) selected from the original voxel-beamlet matrix based on comparisons performed between the columns in the original voxel-beamlet matrix or (ii) created by combining at least some of the columns in the original voxel-beamlet matrix, in a matrix condensing process. The condensing step further includes extracting the protobeams from the original voxel-beamlet matrix. The condensing step also includes storing the protobeams as the reduced dataset in the memory device.

Extracting protobeams for cancer radiation therapy

A method and system are provided. The method includes splitting, by a processor based on radiation beamlet contributions to neighboring voxels, at least one row in a voxel-beamlet matrix and corresponding elements of a target dose vector prior to, and in preparation for, a regression operation. The voxel-beamlet matrix has a row for each of a plurality of voxels in a three-dimensional patient volume and a column for each of a plurality of radiation beamlets. The target dose vector represents a desired energy amount for each of the plurality of voxels in the three-dimensional patient volume. The target dose vector corresponds voxel-by-voxel to rows in the voxel-beamlet matrix. The method further includes performing, by the processor, the regression operation on the voxel-beamlet matrix and target dose vector to obtain beamlet weights. The method also includes determining an actual radiation dosing scheme for a given patient based on the beamlet weights.

Radiation therapy treatment planning using regression

A slew-based effective capacitance (C) is used to compute gate output slew during early synthesis of an integrated circuit design. A &#x3c0; model is constructed for the gate and reduced to two parameters which are used to compute a slew value for the model, given a slew definition. A capacitance coefficient is then calculated as a function of this slew value. The effective capacitance is the product of the coefficient and the total capacitance of the &#x3c0; model. The output slew of the gate may in turn be computed using the slew-based C. The coefficient may be computed by iteratively solving an equation representing output voltage over time dependent on the first and second parameters, by directly solving a closed-form equation which is a function of the first and second parameters, or by looking up the capacitance coefficient in a table indexed by the first and second parameters.

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A method, computer program product, and data processing system for performing an improved optical proximity correction are disclosed, which better respect the electrical properties of the device being manufactured. A preferred embodiment of the present invention performs OPC by first dividing the perimeter of a mask region into a plurality of segments, then grouping the segments into at least two distinct groups, wherein segments in the first of these groups are adjusted in position so as to minimize edge placement error (EPE) when the photolithography using the mask is simulated. Segments in the second group are adjusted in position so as to minimize cumulative error in a dimension spanning the region, wherein the span of such dimension extends from segments in the first group to segments in the second group. Correction so obtained by this process more readily preserves the intended electrical behavior of the original device design.

Optical proximity correction for improved electrical characteristics

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 mechanism is provided for statistical determination of power circuit connectivity based on signal detection in a circuit. Signal data from the circuit gathered and a determination is made as to whether a signal of interest is present in the gathered signal data from the circuit using a statistical analysis of the gathered signal data. The statistical analysis comprises using a mean current value and statistical deviation of the current value of the signal data over a predetermined period of time to compute a confidence range. The confidence range is compared to a first threshold and a second threshold. A determination is made that the signal is present in response to the confidence range being above the first threshold. A determination is made that the signal is not present in response to the confidence range being below the second threshold.

Statistical determination of power-circuit connectivity

A method, system, and computer program product for visualizing sensitivity information in integrated circuit (IC) design are provided in the illustrative embodiments. A plurality of sensitivity information corresponding to a first component in the IC design is received, wherein the plurality of sensitivity information includes a first sensitivity information indicating a first variation in a first electrical characteristic of a group of components as a result of a variation in an electrical characteristic of the first component. A plurality of aspects of the first sensitivity information are rendered in visual form to form a first visualization. The first visualization is presented on a schematic view of the IC design in an IC design tool such that the first sensitivity information is visually associated with the first component in the IC design.

Visualizing sensitivity information in integrated circuit design

A method and test system for fast determination of parameter variation statistics provides a mechanism for determining process variation and parameter statistics using low computing power and readily available test equipment. A test array having individually selectable devices is stimulated under computer control to select each of the devices sequentially. A test output from the array provides a current or voltage that dependent on a particular device parameter. The sequential selection of the devices produces a voltage or current waveform, characteristics of which are measured using a digital multi-meter that is interfaced to the computer. The rms value of the current or voltage at the test output is an indication of the standard deviation of the parameter variation and the DC value of the current or voltage is an indication of the mean value of the parameter.

Method and test system for fast determination of parameter variation statistics

Correlated failure distribution for memory arrays having different groupings of memory cells is estimated by constructing memory unit models for the groupings based on multiple parameters, establishing failure conditions of the memory unit model using fast statistical analysis, calculating a fail boundary of the parameters for each memory unit model based on its corresponding failure conditions, and constructing memory array models characterized by the fail boundaries. Operation of a memory array model is repeatedly simulated with random values of the parameters assigned to the memory cells and peripheral logic elements to identify memory unit failures for each simulated operation. A mean and a variance is calculated for each memory array model, and an optimal architecture can thereafter be identified by selecting the grouping exhibiting the best mean and variance, subject to any other circuit requirements such as power or area.

Methodology for correlated memory fail estimations

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Austin, TX, United States of America

Sani Richard Nassif