Advanced Micro Devices Corporation

International Business Machines Corporation

Spansion Llc.

Alpha & Omega Semiconductor Corporation

Monterey Research, LLC

Hunteck Semiconductor (shanghai) Co. Ltd.

Other

Microsoft Technology Licensing, LLC

Fujitsu Corporation

Amazon Technologies, Inc.

Globalfoundries Inc.

Cypress Semiconductor Corporation

Infineon Technologies LLC

Fujitsu Amd Semiconductor Limited

Huntech Semiconductor (shanghai) Co. Ltd

Fei Wang

Lynne A Okada

Ramkumar Subramanian

Calvin T Gabriel

David Keating Foote

Jianying Hu

Christopher F Lyons

Scott Allan Bell

Harry Jay Levinson

Khanh B Nguyen

Chih Yuh Yang

Steven C Avanzino

Lu You

Todd P Lukanc

Minh Van Ngo

Bharath Rangarajan

Mark S Chang

Angela Tai Hui

Yu Nan Sun

James K Kai

Tanveer Fathima Syeda-Mahmood

William George En

David James Beymer

Ping Zhang

Suzette Keefe Pangrle

Darrell M Erb

Mark T Ramsbey

Simon S Chan

Wenge Yang

Jun Li Wang

Hiroyuki Kinoshita

Dawn M Hopper

Nan Cao

Shenqing Fang

Geetika Tewari Lakshmanan

Minh Quoc Tran

YongZhong Hu

Michelle Xue Zhou

Jimeng Sun

Chi K Chang

Jeffrey Allan Shields

Kashmir S Sahota

Darin A Chan

Jun Hu

Susan H Chen

Bhanwar Singh

Sameer S Haddad

Wei Zhang

Richard J Huang

Pin-Chin Connie Wang

Ching-Yung Lin

George Jonathan Kluth

Alexander H Nickel

Bryan K Choo

Stephen Keetai Park

Allison Holbrook

Wen Yu

Jenny S Li

Robin W Cheung

Sunil D Mehta

Jun Zhai

Mark W Randolph

Subhash Gupta

Unsoon Kim

Liangzhao Zeng

Barton Allen Smith

Hongfei Li

Kun Bai

Hung-Sheng Chen

Zhen Wen

Tatsuya Kajita

Ming Li

Anup Bhalla

Hamza Yilmaz

Sergey D Lopatin

Matthew S Buynoski

Sik Lui

Yi Zhang

Jun Lu

Wei Tan

Kuo-Tung Chang

John JianShi Wang

Jalal U Mahmud

Amit P Marathe

Nicholas H Tripsas

Ning Cheng

Lin Luo

Connie P Wang

Lewis N Shen

Kai Yang

Richard Paul Kingsborough

Rinji Sugino

Rong Nickle Chang

Kenney Ng

Juri Krieger

Vinod Muthusamy

Liang Gou

John Erik Wittern

Raymond T Lee

Yinglong Xia

A semiconducting device utilizing air-gaps for inter-layer insulation and methods of producing the device are described. The device may be produced by forming a sacrificial layer between two structures. A porous membrane layer is then formed over the sacrificial layer. The membrane layer is porous to an etch product, which allows for the subsequent etching of the sacrificial layer leaving an air gap between the device structures and the membrane intact. The device may also include a cap layer formed above the device structures and the membrane.

Inter-layer insulator for electronic devices and apparatus for forming same

Techniques are described that facilitate determining potential cancer gene therapy targets by joint modeling of cancer survival events. In one embodiment, a computer-implemented comprises employing, by a device operatively coupled to a processor, a multi-task learning model to determine active genetic factors respectively associated with different types of cancer based on cancer survival data and patient genomic data for groups of patients that respectively survived the different types of cancer. The computer-implemented method further comprises, determining, by the device, common active genetic factors of the active genetic factors that are shared between two or more types of cancer of the different types of cancer.

Determining potential cancer therapeutic targets by joint modeling of survival events

This invention discloses a semiconductor power device formed on a semiconductor substrate comprises an active cell area and a termination area disposed near edges of the semiconductor substrate. The termination area comprises a plurality of duplicated units wherein each unit includes at least two trenches filled with a conductive trench material having a mesa area between adjacent trenches wherein the trenches and the mesa areas within each of the duplicated units are electrically shunt together. In the termination area each of the trenches in the duplicated units has a buried guard ring dopant region disposed below a bottom surface of the trenches.

Edge termination designs for semiconductor power devices

Various embodiments predict drug-disease associations. In one embodiment, a plurality of disease similarity matrices and a plurality of disease similarity matrices are accessed. Each of the plurality of drug similarity matrices is associated with a different drug information source. Each of the plurality of disease similarity matrices is associated with a different disease information source. A known drug-disease association matrix is also accessed. The known drug-disease association matrix indicates if a given drug identified is known to treat a given disease. At least one drug-disease association prediction is generated based on the plurality of drug similarity matrices, the plurality of disease similarity matrices, and the known drug-disease association matrix. The at least one drug-disease association prediction identifies a previously unknown association between a given drug and a given disease, and a probability that the given disease is treatable by the given drug.

Generating drug repositioning hypotheses based on integrating multiple aspects of drug similarity and disease similarity

Methods and systems for pruning process execution logs include learning a predictive model from a set of execution traces that characterize a process, where the predictive model determines a likelihood of a given instance reaching a specified outcome; identifying attributes in the predictive model that fall below a threshold measure of relevance to the specified outcome using a processor; and removing the identified attributes from the set of execution traces.

Pruning process execution logs

Aspects of the present invention include a method, system and computer program product. The method includes identifying, by a processor, a set of global risk factors for a target event using training patients, and providing, by the processor, a disease progression timeline with defined time stamps by aligning longitudinal data of the training patients based on the defined time stamp of risk targets. The method also includes positioning, by the processor, a target patient at one of the defined time stamps on the disease progression timeline, and identifying, by the processor, at least one of the training patients similar to the target patient with the same one of the defined time stamps on the disease progression timeline. The method further includes calculating, by the processor, a time-varying predictive pattern of at least a portion of the global set of risk factors for the target patient along the disease progression timeline.

Identifying personalized time-varying predictive patterns of risk factors

Techniques for improving content load time are described. A method comprises: receiving, from a search service, a plurality of search results in response to a search query, wherein each of the plurality of search results includes a result link pointing to an object stored on a content source; receiving, from the search service, auxiliary information indicating that, for at least one of the plurality of search results, a copy of at least a portion of the object that the result link of the at least one search result points to is cached within a cache service associated with the search service; and retrieving, from the cache service, the cached copy of at least the portion of the object in response to a selection of the result link of the at least one search result.

Content caching for fast load time

A system and method for patient stratification include determining a first set of patient groups from patients in a patient similarity graph based on a similarity structure of the patient similarity graph. A second set of patient groups is identified based on expert domain knowledge associated with the patients. Patients in the first set and the second set are aligned using a processor to stratify patients.

Patient risk stratification by combining knowledge-driven and data-driven insights

A semiconductor power device includes a plurality of power transistor cells each having a trenched gate disposed in a gate trench opened in a semiconductor substrate wherein a plurality of the trenched gates further include a shielded bottom electrode disposed in a bottom portion of the gate trench electrically insulated from a top gate electrode disposed at a top portion of the gate trench by an inter-electrode insulation layer. At least one of the shielded bottom electrode is connected a source metal and at least one of the top electrodes in the gate trench is connected to a source metal of the power device.

SGT MOSFET with adjustable CRSS and CISS

An embodiment of the invention receives input including a list of drugs, drug characteristics of each drug, and known drug-disease associations including a disease and a drug having a threshold efficacy for treating the disease. For each drug in the list of drugs, a processor predicts whether the drug meets a threshold efficacy for treating a first disease based on the drug characteristics and the drug-disease associations. For each drug in the list of drugs, the processor predicts whether the drug meets a threshold efficacy for treating a second disease based on the drug characteristics and the predicting of whether the drug meets the threshold efficacy for treating the first disease. Output is generated output based on the predictions, the output including an identified drug-disease association, an identified disease-disease association, an identified chemical fingerprint for the first disease, and an identified chemical fingerprint for the second disease.

Evidence boosting in rational drug design and indication expansion by leveraging disease association

A system and method for providing a temporally dynamic model parameter include building a model parameter by minimizing a loss function based on patient measurements taken at a plurality of time points. Temporally related values of the model parameter are identified, using a processor, having a same type of patient measurement taken at different time points. At least one value of the model parameter and temporally related values of the at least one value are selected to provide a temporally dynamic model parameter.

Dynamic identification of the biomarkers leveraging the dynamics of the biomarker

An integrated circuit memory device, in one embodiment, includes a substrate having a plurality of bit lines. A first and second inter-level dielectric layer are successively disposed on the substrate. Each of a plurality of source lines and staggered bit line contacts extend through the first inter-level dielectric layer. Each of a plurality of source line vias and a plurality of staggered bit line vias extend through the second inter-level dielectric layer to each respective one of the plurality of source lines and the plurality of staggered bit line contacts. The source lines and staggered bit line contacts that extend through the first inter-level dielectric layer are formed together by a first set of fabrication processes. The source line vias and staggered bit line contacts that extend through the second inter-level dielectric layer are also formed together by a second set of fabrication processes.

Memory device interconnects and method of manufacture

At integrated circuit memory device, in one embodiment, includes a substrate having a plurality of bit lines. A first and second inter-level dielectric layer are successively disposed on the substrate. Each of a plurality of source lines and staggered bit line contacts extend through the first inter-level dielectric layer. Each of a plurality of source line vias and a plurality of staggered bit line vias extend through the second inter-level dielectric layer to each respective one of the plurality of source lines and the plurality of staggered bit line contacts. The source lines and staggered bit line contacts that extend through the first inter-level dielectric layer are formed together by a first set of fabrication processes. The source line vias and staggered bit line contacts that extend through the second inter-level dielectric layer are also formed together by a second set of fabrication processes.

A processor-implemented method, computer program product and system are provided for predicting drug-drug interactions based on clinical side effects. The method includes constructing a drug-drug interactions training dataset that includes pharmaceutical, pharmacokinetic or pharmacodynamics drug-drug interactions from multiple data sources for each of a plurality of drugs. The method also includes constructing side effect features for each of the drugs from side effects associated with the drugs. The method further includes building, using the drug-drug interactions training dataset, a drug-drug interactions classifier that predicts adverse drug-drug interactions for drug pairs derivable from the drugs. The method additionally includes for each of the side effects, building a two-by-two table using the side effect features, and performing a Fisher's exact test using the two-by-two table to determine whether a given one of side effects is differentially shown between positive predicted drug-drug interactions and negative predicted drug-drug interactions.

Predicting drug-drug interactions based on clinical side effects

Growing community of inventors

San Jose, CA, United States of America

Fei Wang