Asml Netherlands B.v.

Globalfoundries Inc.

Apple Inc.

Advanced Micro Devices Corporation

Globalfoundries Singapore Pte. Ltd.

Yi Zou

Chenxi Lin

Jing Su

Daniel A Podhajny

Azat M Latypov

Youping Zhang

Nicholas R Trincia

Joshua A Hoover

Yue Chen

Seul Bi Kim

William Leith

Chad J Miller

Kristen L Cretella

Maxime Philippe Frederic Genin

Yen-Wen Lu

Vito Dai

Yana Cheng

Cyrus Emil Tabery

Wei-Long Wang

Yu Long Cao

Tamer Coskun

Simon Philip Spencer Hastings

Boris Menchtchikov

Stefan Hunsche

Luigi Capodieci

Marinus Jochemsen

Marinus Aart Van Den Brink

Rafael C Howell

Been-Der Chen

Lin Lee Cheong

Quan Zhang

Weixuan Hu

Cong Wu

Mark W Michael

Wim Tjibbo Tel

Duan-Fu Stephen Hsu

Christopher A Spence

Darin A Chan

Robert John Socha

Hakki Ergün Cekli

Marilyn I Wright

Hua Song

Gek Soon Chua

Daan Maurits Slotboom

Yuansheng Ma

Abraham Slachter

Davit Harutyunyan

Lynn Wang

Donna Michael, Legal Representative

Tzu-Chao Chen

Matthijs Cox

Byoung Il Choi

Vadim Yourievich Timoshkov

Liming Wang

Vedant A Dhandhania

Koen Wilhelmus Cornelis Adrianus Van Der Straten

Nicolaas Petrus Marcus Brantjes

Swamy Maddu

Peng Xie

Danning Zhang

Maxim Philippe Frederic Genin

Nicholas R Trinica

A head-mounted device may include a housing with a display that displays images that are viewable from an eye box. A light seal may be coupled the housing and may block outside light from reaching the eye box. The light seal may include a light-emitting fabric. The light-emitting fabric may include light-emitting devices such as light-emitting diodes and/or light guides that produce visual output on the light seal and/or on other portions of the head-mounted device. The visual output may be coordinated with images on the display. Light guides may have light-scattering structures that allow light to escape from the light guides and thereby form illuminated regions on the light-emitting fabric. The light-scattering structures may be aligned with openings in the fabric. Light-emitting diodes may be mounted to the fabric or may emit light through openings in the fabric.

Electronic devices with light-emitting fabrics

Methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of one or more characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective; obtaining a characteristic of each of the process conditions; obtaining a characteristic of each of the hot spots; and training a machine learning model using a training set including the characteristic of one of the process conditions, the characteristic of one of the hot spots, and whether that hot spot is defective under that process condition.

Identification of hot spots or defects by machine learning

A method for calibrating a process model and training an inverse process model of a patterning process. The training method includes obtaining a first patterning device pattern from simulation of an inverse lithographic process that predicts a patterning device pattern based on a wafer target layout, receiving wafer data corresponding to a wafer exposed using the first patterning device pattern, and training an inverse process model configured to predict a second patterning device pattern using the wafer data related to the exposed wafer and the first patterning device pattern.

Machine learning based inverse optical proximity correction and process model calibration

A method for predicting yield relating to a process of manufacturing semiconductor devices on a substrate, the method including: obtaining a trained first model which translates modeled parameters into a yield parameter, the modeled parameters including: a) a geometrical parameter associated with one or more selected from: a geometric characteristic, dimension or position of a device element manufactured by the process and b) a trained free parameter; obtaining process parameter data including data regarding a process parameter characterizing the process; converting the process parameter data into values of the geometrical parameter; and predicting the yield parameter using the trained first model and the values of the geometrical parameter.

Method to predict yield of a device manufacturing process

A method for analyzing a process, the method including obtaining a multi-dimensional probability density function representing an expected distribution of values for a plurality of process parameters; obtaining a performance function relating values of the process parameters to a performance metric of the process; and using the performance function to map the probability density function to a performance probability function having the process parameters as arguments.

Method of manufacturing devices

Methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method involves obtaining a training data set including: (i) a simulated pattern associated with a mask pattern to be printed on a substrate, (ii) inspection data of a printed pattern imaged on the substrate using the mask pattern, and (iii) measured values of a parameter of the patterning process applied during imaging of the mask pattern on the substrate; and training a machine learning model for the patterning process based on the training data set to predict a difference in a characteristic of the simulated pattern and the printed pattern. The trained machine learning model can be used for determining a ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.

Determining pattern ranking based on measurement feedback from printed substrate

A wearable device can provide an audio module that is operable to provide audio output from a distance away from the ears of the user. For example, the wearable device can be worn on clothing of the user and direct audio waves to the ears of the user. Such audio waves can be focused by a parametric array of speakers that limit audibility by others. Thus, the privacy of the audio directed to the user can be maintained without requiring the user to wear audio headsets on, over, or in the ears of the user. The wearable device can further include microphones and/or connections to other devices that facilitate calibration of the audio module of the wearable device. The wearable device can further include user sensors that are configured to detect, measure, and/or track one or more properties of the user.

Wearable device with directional audio

A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method including: obtaining a set of pre-process metrology data; processing the set of pre-process metrology data by decomposing the pre-process metrology data into one or more components which: a) correlate to the performance metric; or b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and applying a trained model to the processed set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.

Method for controlling a manufacturing process and associated apparatuses

Wireless earbuds may be provided with adjustable-shape housings. The housings may have bendable portions. Bendable metal members, hinges, or other flexible structures may be used in forming bendable structures for the earbuds. Electrical components may be covered by a layer of molded foam. A cover such as a fabric cover may be used to cover the molded foam. Spacer fabric or other soft material may be interposed between the fabric cover and the foam. The housing may be bent or otherwise adjusted between two or more states such as a normal, non-sleep, walking state in which the housing is expanded for normal operation while a user is sitting or walking and a sleep state in which the housing is bent to enhance comfort while sleeping. The wireless earbuds may have illumination systems, sensors, and other components.

Earbuds

A method for determining a root cause affecting yield in a process for manufacturing devices on a substrate, the method including: obtaining yield distribution data including a distribution of a yield parameter across the substrate or part thereof; obtaining sets of metrology data, each set including a spatial variation of a process parameter over the substrate or part thereof corresponding to a different layer of the substrate; comparing the yield distribution data and metrology data based on a similarity metric describing a spatial similarity between the yield distribution data and an individual set out of the sets of the metrology data; and determining a first similar set of metrology data out of the sets of metrology data, being the first set of metrology data in terms of processing order for the corresponding layers, which is determined to be similar to the yield distribution data.

Method for determining root cause affecting yield in a semiconductor manufacturing process

An electronic device may be formed from acoustically permeable textile layers and voice coil layers. The voice coil layers may output sound through the textile layers. The textile layers and voice coil layers may be foldable or bent in one or more directions to allow the electronic device to be placed in expanded, collapsed, or folded configurations to allow for improved portability. Additionally, the textile layers and voice coil layers may be folded or bent to place the electronic device in cylindrical, conical, and other configurations to allow for improved sound output from the voice coil layers. The electronic device may also include light-emitting components between the textile layers that emit light through one of the textile layers to form indicators or images for a user of the device. The electronic device may have a plurality of sensors to allow user interactions to control the output of the electronic device.

Electronic device with flexible housings

A method to determine a curvilinear pattern of a patterning device that includes obtaining (i) an initial image of the patterning device corresponding to a target pattern to be printed on a substrate subjected to a patterning process, and (ii) a process model configured to predict a pattern on the substrate from the initial image, generating, by a hardware computer system, an enhanced image from the initial image, generating, by the hardware computer system, a level set image using the enhanced image, and iteratively determining, by the hardware computer system, a curvilinear pattern for the patterning device based on the level set image, the process model, and a cost function, where the cost function (e.g., EPE) determines a difference between a predicted pattern and the target pattern, where the difference is iteratively reduced.

Method for determining curvilinear patterns for patterning device

An accessory for wireless earbuds can include a first receiving portion defining a first cavity sized to receive and retain a first earbud and a second receiving portion defining a second cavity sized to receive and retain a second earbud. Each receiving portion can include a charging component to electrically couple with the respective earbud. The accessory can include a flexible portion connected to the first receiving portion and the second receiving portion, the flexible portion at least partially defining an internal volume, and a battery disposed in the internal volume and electrically coupled with the charging components.

Wireless headphone accessory

Growing community of inventors

Foster City, CA, United States of America

Yi Zou