Applied Materials, Inc.

Arco Chemical Technology, L.p.

Future Wei Technologies, Inc.

Alpha Omega Semiconductor Inc.

Other

Lyondell Chemical Technology, L.p.

Intel Corporation

Huawei Technologies Co., Limited

Bayer Materialscience, LLC

Bayer Antwerpen, J.v.

Wei D Wang

Praburam Gopalraja

Jianming Fu

Fusen E Chen

Zheng Xu

Wei Jiang

Bi Le-Khac

Ashok K Sinha

Girish Anant Dixit

Shao-Hua Guo

Daniel B Pourreau

Srinivas Gandikota

Anantha K Subramani

Anup Bhalla

Daniel Ng

Yi Qiang Su

Jick M Yu

Sankaram Athreya

Sik Lui

John C Forster

Xianmin Tang

Muhammad M Rasheed

Xiaobin Wang

Yong Cao

Ananthkrishna Jupudi

Edward T Shawl

Stephen Herbert Harris

Kevin Vincent Moraes

Thanh X Nguyen

Mahmoud K Faraj

Zhendong Liu

Kishore Lavu

Elmoustapha Ould-Ahmed-Vall

Hua Chung

Eriko Nurvitadhi

Rongjun Wang

Amit Bleiweiss

Deborah T Marr

Ji Pan

Jeffrey J Cook

Srinivas Sridharan

Asit K Mishra

Dheevatsa Mudigere

Dhawal Srivastava

Anavai Ramesh

Mohammad Ashraf Bhuiyan

Md Faijul Amin

Farhad Fadakar

Niharika Maheshwari

Shanti P Patel

L Joseph Junker

The disclosure relates to technology for object detection in which a vision system receives training datasets including a set of two-dimensional (2D) images of the object from multiple views. A set of 3D models is reconstructed from the set of 2D images based on salient points of the object selected during reconstruction to generate one or more salient 3D models of the object that is an aggregation of the salient points of the object in the set of 3D models. A set of training 2D-3D correspondence data are generated between the set of 2D images in a first training dataset of the training datasets and the salient 3D model of the object generated using the first training dataset. A deep neural network is trained using the set of training 2D-3D correspondence data generated using the first training dataset for object detection and segmentation.

Three-dimension (3D) assisted personalized home object detection

An apparatus to facilitate acceleration of machine learning operations is disclosed. The apparatus comprises at least one processor to perform operations to implement a neural network and accelerator logic to perform communicatively coupled to the processor to perform compute operations for the neural network.

Machine learning accelerator mechanism

Provided are an apparatus and a method of multi-stage image recognition. For the multi-stage image recognition, categorized object data is received from a first deep neural network. A second deep neural network is trained on subcategory customization data that relates to a non-ideal environment when the second deep neural network produces invalid subcategorized object data from the categorized object data, and generates an image recognition result using the second deep neural network as trained.

Multi-stage image recognition for a non-ideal environment

A robotic device is disclose as having deep reinforcement learning capability. The device includes non-transitory memory comprising instructions and one or more processors in communication with the memory. The instructions cause the one or more processors to receive a sensing frame, from a sensor, comprising an image. The processors then determine a movement transition based on the sensing frame and the deep reinforcement learning, wherein the deep reinforcement learning uses at least one of a map coverage reward, a map quality reward, or a traversability reward to determine the movement transition. The processors then update an area map based on the sensing frame and the deep reinforcement learning using a visual simultaneous localization and mapping (SLAM) process to determine the map updates.

Simultaneous localization and mapping with reinforcement learning

A system and method of tracking an object and navigating an object tracking robot includes receiving tracking sensor input representing the object and an environment at multiple times, responsive to the tracking sensor input, calculating positions of the robot and the object at the multiple times, and using a computer implemented deep reinforcement learning (DRL) network trained as a function of tracking quality rewards and robot navigation path quality rewards, the DRL network being responsive to the calculated positions of the robot and the object at the multiple times to determine possible actions specifying movement of the object tracking robot from a current position of the robot and target, determine quality values (Q-values) for the possible actions, and select an action as a function of the Q-values. A method of training the DRL network is also included.

Robot navigation and object tracking

A computer-implemented method for a three-dimensional (3D) reconstruction of a dynamic scene includes receiving a plurality of color image sequences from a plurality of color imaging sensors, and at least one depth image sequence from at least one depth imaging sensor, where a color imaging sensor quantity is larger than a depth imaging sensor quantity. A plurality of calibrated color image sequences and at least one calibrated depth image sequence are generated based on the plurality of color imaging sequences and the at least one depth image sequence. A plurality of initial 3D patches is constructed using the plurality of calibrated color image sequences and the at least one calibrated depth image sequence. A 3D patch cloud is generated by expanding the plurality of initial 3D patches.

Three-dimensional (3D) reconstructions of dynamic scenes using a reconfigurable hybrid imaging system

A method for fine-grained object recognition in a robotic system is disclosed that includes obtaining an image of an object from an imaging device. Based on the image, a deep category-level detection neural network is used to detect pre-defined categories of objects. A feature map is generated for each pre-defined category of object detected by the deep category-level detection neural network. Embedded features are generated, based on the feature map, using a deep instance-level detection neural network corresponding to the pre-defined category of the object, wherein each pre-defined category of an object comprises a corresponding different instance-level detection neural network. An instance-level of the object is determined based on classification of the embedded features.

Fine-grained object recognition in robotic systems

A method and apparatus are described for reducing particle contamination in a plasma processing chamber. In one embodiment, a pasting disk is provided which includes a disk-shaped base of high-resistivity material that has an electrically conductive pasting material layer applied to a top surface of the base so that the pasting material layer partially covers the top surface of the base. The pasting disk is sputter etched to deposit conductive pasting material over a wide area on the interior surfaces of a plasma processing chamber while minimizing deposition on dielectric components that are used to optimize the sputter etch process during substrate processing.

Apparatus and method for depositing electronically conductive pasting material

A semiconductor die with integrated MOSFET and diode-connected enhancement mode JFET is disclosed. The MOSFET-JFET die includes common semiconductor substrate region (CSSR) of type-1 conductivity. A MOSFET device and a diode-connected enhancement mode JFET (DCE-JFET) device are located upon CSSR. The DCE-JFET device has the CSSR as its DCE-JFET drain. At least two DCE-JFET gate regions of type-2 conductivity located upon the DCE-JFET drain and laterally separated from each other with a DCE-JFET gate spacing. At least a DCE-JFET source of type-1 conductivity located upon the CSSR and between the DCE-JFET gates. A top DCE-JFET electrode, located atop and in contact with the DCE-JFET gate regions and DCE-JFET source regions. When properly configured, the DCE-JFET simultaneously exhibits a forward voltage Vf substantially lower than that of a PN junction diode while the reverse leakage current can be made comparable to that of a PN junction diode.

Battery charging circuit with serial connection of MOSFET and an enhancement mode JFET configured as reverse blocking diode with low forward voltage drop

Apparatus and method for depositing electrically conductive pasting material

Embodiments described herein provide a semiconductor device and methods and apparatuses of forming the same. The semiconductor device includes a substrate having a source and drain region and a gate electrode stack on the substrate between the source and drain regions. In one embodiment, the method includes positioning a substrate within a processing chamber, wherein the substrate includes a source and drain region, a gate dielectric layer between the source and drain regions, and a conductive film layer on the gate dielectric layer. The method also includes depositing a refractory metal nitride film layer on the conductive film layer, depositing a silicon-containing film layer on the refractory metal nitride film layer, and depositing a tungsten film layer on the silicon-containing film layer.

Low resistivity tungsten PVD with enhanced ionization and RF power coupling

Semiconductor device die with integrated MOSFET and low forward voltage diode-connected enhancement mode JFET and method

Embodiments described herein provide a semiconductor device and methods and apparatuses of forming the same. The semiconductor device includes a substrate having a source and drain region and a gate electrode stack on the substrate between the source and drain regions. The gate electrode stack includes a conductive film layer on a gate dielectric layer, a refractory metal nitride film layer on the conductive film layer, a silicon-containing film layer on the refractory metal nitride film layer, and a tungsten film layer on the silicon-containing film layer. In one embodiment, the method includes positioning a substrate within a processing chamber, wherein the substrate includes a source and drain region, a gate dielectric layer between the source and drain regions, and a conductive film layer on the gate dielectric layer. The method also includes depositing a refractory metal nitride film layer on the conductive film layer, depositing a silicon-containing film layer on the refractory metal nitride film layer, and depositing a tungsten film layer on the silicon-containing film layer.

Semiconductor device with gate electrode stack including low resistivity tungsten and method of forming

A semiconductor power device supported on a semiconductor substrate includes an electrostatic discharge (ESD) protection circuit disposed on a first portion of patterned ESD polysilicon layer on top of the semiconductor substrate. The semiconductor power device further includes a second portion of the patterned ESD polysilicon layer constituting a body implant ion block layer for blocking implanting body ions to enter into the semiconductor substrate below the body implant ion block layer. In an exemplary embodiment, the electrostatic discharge (ESD) polysilicon layer on top of the semiconductor substrate further covering a scribe line on an edge of the semiconductor device whereby a passivation layer is no longer required manufacturing the semiconductor device for reducing a mask required for patterning the passivation layer.

Reduced mask configuration for power mosfets with electrostatic discharge (ESD) circuit protection

A method of depositing a bilayer of tungsten over tungsten nitride by a plasma sputtering process in which krypton is used as the sputter working gas during the tungsten deposition. Argon may be used as the sputtering working gas during the reactive sputtering deposition of tungsten nitride. The beneficial effect of reduction of tungsten resistivity is increased when the thickness of the tungsten layer is less than 50 nm and further increased when less than 35 nm. The method may be used in forming a gate stack including a polysilicon layer over a gate oxide layer over a silicon gate region of a MOS transistor in which the tungsten nitride acts as a barrier. A plasma sputter chamber in which the invention may be practiced includes gas sources of krypton, argon, and nitrogen.

Krypton sputtering of low resistivity tungsten

Growing community of inventors

San Jose, CA, United States of America

Wei D Wang