Applied Materials, Inc.

Samer Banna

Kyle Tantiwong

Valentin Nikolov Todorow

Vladimir Knyazik

Imad Yousif

Mehdi Vaez-Iravani

Waheb Bishara

Yan Rozenzon

Bojenna Keating

Mukhles Sowwan

Todd J Egan

Gregory Kirk

Dermot Cantwell

Gary E Dickerson

Kartik Ramaswamy

Dmitry Lubomirsky

Andrew Nguyen

Gary Leray

Hiroji Hanawa

Tza-Jing Gung

Zhigang Chen

Ankur Agarwal

Yaoming Shen

Abraham Ravid

Lior Engel

Tse-Chiang Wang

Seng (victor) Keong Lim

Ofer Yuli

Albert Wang

Kenneth S Collins

Jennifer Y Sun

Shahid Rauf

Douglas A Buchberger, Jr

Nicolas Posseme

Michael D Willwerth

Dan A Marohl

Joseph F AuBuchon

Lei Lian

Konstantin Makhratchev

Omkaram Nalamasu

Olivier Joubert

Lawrence Wong

Maxime Darnon

Xing Lin

Nirmalya Maity

Martin Jeff Salinas

Alvaro Garcia De Gorordo

Stephen Yuen

Laurent J Vallier

Anchel Sheyner

Dong Wu

Mark Markovsky

Ryan Giar

Embodiments of automated closed apparatus for cell therapy manufacturing are provided herein. In some embodiments, an automated closed apparatus for cell therapy manufacturing includes: a master device having a master controller for processing control programs for a variety of cell types; an input device fluidly coupled to the master device, wherein the input device is configured to feed an initial plurality of cells to the master device; one or more auxiliary devices each configured to perform one or more cell therapy manufacturing steps to the initial plurality of cells to form a final plurality of cells; and an output device coupled to the master device configured to collect the final plurality of cells from the master device.

Automated closed system for cell therapy manufacturing

Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., wafer-to-wafer, lot-to-lot, chamber-to-chamber etc.) using machine learning techniques.

Adaptive control of variability in device performance in advanced semiconductor processes

The present disclosure relates to methods and apparatus for forming a thin-form-factor semiconductor device package. In certain embodiments, a glass or silicon substrate is patterned by laser ablation to form structures for subsequent formation of interconnections therethrough. The substrate is thereafter utilized as a frame for forming a semiconductor device package, which may have one or more embedded dies therein. In certain embodiments, an insulating layer is formed over the substrate by laminating a pre-structured insulating film thereon. The insulating film may be pre-structured by laser ablation to form structures therein, followed by selective curing of sidewalls of the formed structures.

Semiconductor device packaging methods

The present disclosure relates to semiconductor core assemblies and methods of forming the same. The semiconductor core assemblies described herein may be utilized to form semiconductor package assemblies, printed circuit board (PCB) assemblies, PCB spacer assemblies, chip carrier assemblies, intermediate carrier assemblies (e.g., for graphics cards), and the like. In one embodiment, a substrate core (e.g., a core structure) is implanted with dopants to achieve a desired bulk resistivity or conductivity. One or more conductive interconnections are formed in the substrate core and one or more redistribution layers are formed on surfaces thereof. The substrate core may thereafter be utilized as a core structure for a semiconductor package, PCB, PCB spacer, chip carrier, intermediate carrier, or the like.

Adapting electrical, mechanical, and thermal properties of package substrates

Embodiments of apparatus and methods for counting cells in a liquid sample are provided herein. In some embodiments, an apparatus for counting cells in a liquid sample includes: a flow-splitting chamber fluidly coupled to a collection chamber; an input tube configured to deliver a liquid sample to the flow-splitting chamber; a spaced apart array of posts along a flow path configured to redirect the liquid sample into a plurality of streams; a plurality of sensing zones corresponding to the plurality of streams; and a plurality of sensing electrodes, wherein each sensing electrode is disposed in a corresponding sensing zone of the plurality of sensing zones and configured to detect a change in electrical impedance as the liquid sample flows through the plurality of sensing zones.

High throughput compact microfluidic cell counter

Methods and apparatus of bioreactors for therapeutic cells manufacturing are provided herein. In some embodiments, a bioreactor includes: an upper bioreactor reservoir configured to perform multiple cell therapy manufacturing process steps including genetic modification and expansion to a plurality of cells disposed therein, wherein the upper bioreactor reservoir includes a plurality of ports for delivering fluids into and out of the upper bioreactor reservoir; a lower bioreactor compartment configured to hold a suspension comprising a molecular species; and a membrane disposed between the lower bioreactor compartment and the upper bioreactor reservoir, wherein the membrane includes a plurality of micro-straws extending through the membrane and into the upper bioreactor reservoir to transfect the plurality of cells with the molecular species.

All-in-one bioreactor for therapeutic cells manufacturing

A gas distribution hub for a plasma chamber. The hub has a nozzle including a plurality of inner gas injection passage and a plurality of outer gas injection passages. The first plurality of gas injection passages are angularly spaced-apart arcuate channels at a first radial distance from a center of the hub, and the second plurality of gas injection passages are angularly spaced apart arcuate channels at a different second radial distance from the center of the hub.

Gas hub for plasma reactor

An annular lid plate of a plasma reactor has upper and lower layers of gas distribution channels distributing gas along equal length paths from gas supply lines to respective gas distribution passages of a ceiling gas nozzle.

Plasma reactor with highly symmetrical four-fold gas injection

A plurality of energy filter values are obtained using a model that simulates potential distribution within a 3D feature when an electron beam of an SEM impinges on a selected area that includes the 3D feature. A correspondence is extracted between the plurality of energy filter values and respective depths of the 3D feature along a longitudinal direction by analyzing the simulated potential distribution. A plurality of SEM images of the 3D feature corresponding to the plurality of energy filter values are obtained. The plurality of SEM images are associated with their respective depths based on the extracted correspondence between the plurality of energy filter values and the respective depths. A composite 3D profile of the 3D feature is generated from the plurality of SEM images obtained from various depths of the 3D feature.

High-resolution three-dimensional profiling of features in advanced semiconductor devices in a non-destructive manner using electron beam scanning electron microscopy

A gas injection system includes (a) a side gas plenum, (b) a plurality of N gas inlets coupled to said side gas plenum, (c) plural side gas outlets extending radially inwardly from said plenum, (d) an N-way gas flow ratio controller having N outputs coupled to said N gas inlets respectively, and (e) an M-way gas flow ratio controller having M outputs, respective ones of said M outputs coupled to said tunable gas nozzle and a gas input of said N-way gas flow ratio controller.

Nozzle for multi-zone gas injection assembly

A system for processing a substrate is provided. The system includes a process chamber including one or more sidewalls enclosing a processing region; and a substrate support. The system further includes a passageway connected to the process chamber; and a first particle detector disposed at a first location along the passageway. The first particle detector includes an energy source configured to emit a first beam; one or more optical devices configured to direct the first beam along one or more paths, where the one or more paths extend through at least a portion of the passageway. The first particle detector further includes a first energy detector disposed at a location other than on the one or more paths. The system further includes a controller configured to communicate with the first particle detector, wherein the controller is configured to identify a fault based on signals received from the first particle detector.

Particle detection for substrate processing

Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., to account for chamber-to-chamber variability) using machine learning techniques.

Adaptive chamber matching in advanced semiconductor process control

A semiconductor device is scanned by an electron beam of a scanning electron microscope (SEM). The area includes a three-dimensional (3D) feature having a top opening and a sidewall. The 3D feature is imaged while varying an energy value of the electron beam. The electron beam impinges at a first point within a selected area of the semiconductor device and interacts with the sidewall, wherein the first point is at a distance away from an edge of the top opening. Based on change in a signal representing secondary electron yield at the edge as the energy value of the electron beam is varied during the SEM imaging, it is determined whether the sidewall is occluded from a line-of-sight of the electron beam. A slope of the sidewall may be determined by comparing measured signals with simulated waveforms corresponding to various slopes.

Use of electron beam scanning electron microscopy for characterization of a sidewall occluded from line-of-sight of the electron beam

Adaptive control of wafer-to-wafer variability in device performance in advanced semiconductor processes

This disclosure describes methods and systems for building a spatial model to predict performance of processing chamber, and using the spatial model to converge faster to a desired process during the process development phase. Specifically, a machine-learning engine obtains an empirical process model for a given process for a given processing chamber. The empirical process model is calibrated by using the in-line metrology data as reference. A predictive model is built by refining the empirical process model by a machine-learning engine that receives customized metrology data and outputs one or more spatial maps of the wafer for one or more dimensions of interest across the wafer without physically processing any further wafers, i.e. by performing spatial digital design of experiment (Spatial DoE).

Predictive spatial digital design of experiment for advanced semiconductor process optimization and control

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San Jose, CA, United States of America

Samer Banna