Lam Research Corporation

Richard Alan Gottscho

Vahid Vahedi

Adrien Lavoie

Andrew D Bailey, Iii

Alex Paterson

Ali Shajii

Saravanapriyan Sriraman

Siddharth P Nagarkatti

Andrew Cowe

Souheil Benzerrouk

William Robert Entley

Harmeet Singh

Jeffrey S Marks

Yang Pan

Jengyi Yu

Timothy William Weidman

Richard Stephan Wise

Dennis Michael Hausmann

Artur Kolics

Bhadri N Varadarajan

George Andrew Antonelli

Robert Steger

Sirish K Reddy

Mehmet Derya Tetiker

Thomas Joseph Knisley

Rajinder Dhindsa

John Patrick Holland

Mukund Srinivasan

Peter L G Ventzek

Wenbing Yang

Boris Volosskiy

Samantha Siamhwa Tan

Da Li

Daniel Peter

Yiwen Fan

Sivananda K Kanakasabapathy

Qinghuang Lin

John Edward Daugherty

Neil M Benjamin

Nicolas J Bright

Samantha S H Tan

Joydeep Guha

Jerome S Hubacek

David J Cooperberg

Mohammed Haroon Alvi

Thorsten B Lill

Michal Danek

Andreas Fischer

Ivan L Berry, Iii

Nerissa Sue Draeger

Tom A Kamp

Juline Shoeb

Keith J Hansen

Keith B Wells

Aaron Eppler

Yoko Yamaguchi

Chris Lee

Kapil Umesh Sawlani

Hossein Sadeghi

Roderick W Boswell

Maria E Barone

Steve Lee

Development of resists are useful, for example, to form a patterning mask in the context of high-resolution patterning. Development can be accomplished using a halide-containing chemistry such as a hydrogen halide. A metal-containing resist film may be deposited on a semiconductor substrate using a dry or wet deposition technique. The resist film may be an EUV-sensitive organo-metal oxide or organo-metal-containing thin film resist. After exposure, the photopatterned metal-containing resist is developed using wet or dry development.

Photoresist development with halide chemistries

Methods for making thin-films on semiconductor substrates, which may be patterned using EUV, include: depositing the organometallic polymer-like material onto the surface of the semiconductor substrate, exposing the surface to EUV to form a pattern, and developing the pattern for later transfer to underlying layers. The depositing operations may be performed by chemical vapor deposition (CVD), atomic layer deposition (ALD), and ALD with a CVD component, such as a discontinuous, ALD-like process in which metal precursors and counter-reactants are separated in either time or space.

Integrated dry processes for patterning radiation photoresist patterning

A sorption structure used in a plasma process chamber includes an inner layer having one or more heating elements to heat the sorption structure, a middle section having a lattice structure and a coolant flow delivery network through which a coolant circulates to cool the sorption structure, and a vacuum flow network that is connected to a vacuum line to create low pressure vacuum. The lattice structure includes network of openings defined in a plurality of layers. The inner layer is disposed adjacent to the middle section and an outer layer of the lattice structure faces an interior region of the chamber. The openings in the layers of the lattice structure progressively increase in size from the inner layer to the outer layer. The lattice structure is used to adsorb by-products released in the process chamber and the vacuum flow network is used to remove the by-products.

Sorption chamber walls for semiconductor equipment

Etch in a thermal etch reaction is predicted using a machine learning model. Chemical characteristics of an etch process and associated energies in one or more reaction pathways of a given thermal etch reaction are identified using a quantum mechanical simulation. Labels indicative of etch characteristics may be associated with the chemical characteristics and associated energies of the given thermal etch reaction. The machine learning model can be trained using chemical characteristics and associated energies as independent variables and labels as dependent variables across many different etch reactions of different types. When chemical characteristics and associated energies for a new thermal etch reaction are provided as inputs in the machine learning model, the machine learning model can accurately predict etch characteristics of the new thermal etch reaction as outputs.

Predicting etch characteristics in thermal etching and atomic layer etching

A system for use in processing a substrate is provided. One system includes a chamber having an interior region that is exposed to plasma when processing a substrate. The internal region includes surfaces of parts of the chamber. A controller is interfaced with the chamber and includes a detector to enable control of a scope. The scope is configured for insertion into the chamber to inspect the interior region of the chamber without breaking a vacuum of the chamber. The detector includes an optical processor for identifying a characteristic of material present on a surface being inspected via the scope. A tool model processor is configured to receive information regarding the identified characteristic of the material present on the surface and interface with a tool model for the chamber to identify an adjustment to a parameter of a process to be performed using the chamber. The adjustment is configured to compensate for an anticipated drift in the process based on the identified characteristic of the material present on the surface and data from the tool model.

Plasma processing system having an inspection tool and controller that interfaces with a tool model

Defects on a substrate comprising electronic components can be classified with a computational defect analysis system that may be implemented in multiple stages. For example, a first stage classification engine may process metrology data to produce an initial classification of defects. A second stage classification engine may use the initial classification, along with manufacturing information and/or prior defect knowledge to output probabilities that the defects are caused by one or more potential sources.

Defect classification and source analysis for semiconductor equipment

Vacuum-integrated photoresist-less methods and apparatuses for forming metal hardmasks can provide sub-30 nm patterning resolution. A metal-containing (e.g., metal salt or organometallic compound) film that is sensitive to a patterning agent is deposited on a semiconductor substrate. The metal-containing film is then patterned directly (i.e., without the use of a photoresist) by exposure to the patterning agent in a vacuum ambient to form the metal mask. For example, the metal-containing film is photosensitive and the patterning is conducted using sub-30 nm wavelength optical lithography, such as EUV lithography.

Vacuum-integrated hardmask processes and apparatus

A consumable part having a body with a surface is configured to be exposed to plasma during processing in a chamber. The consumable part includes a trigger feature disposed in the body. The trigger feature includes a void, and the void is an identifiable feature on the surface of the body to identify a wear level of the consumable part. The wear level is correlated to a lifetime remaining for the consumable part.

Estimation of lifetime remaining for a consumable-part in a semiconductor manufacturing chamber

A system for controlling processing state of a plasma process is provided. One example system includes a plasma reactor having a plurality of tuning knobs for making settings to operational conditions of the plasma reactor. A plurality of sensors of the plasma reactor is included, where each of the plurality of sensors is configured to produce a data stream of information during operation of the plasma reactor for carrying out the plasma process. A controller of the plasma reactor is configured to execute a multivariate processing that is configured to use as input desired processing state values that define intended measurable conditions within a processing environment of the plasma reactor and identify current plasma processing values. The multivariate processing uses a machine learning engine that receives as inputs the desired processing state values and data streams from the plurality of sensors during processing of the plasma process. The machine learning engine is configured to identify current processing state values used to produce a compensation vector, such that the compensation vector defines differences between the desired process state values and the current processing state values. The controller is further configured to execute a compensation processing operation that transforms the compensation vector expressed in terms of measured conditions within the processing environment to changes of specific one or more of the tuning knobs of the plasma reactor.

System implementing machine learning in complex multivariate wafer processing equipment

Disclosed are methods of generating a proximity-corrected design layout for photoresist to be used in an etch operation. The methods may include identifying a feature in an initial design layout, and estimating one or more quantities characteristic of an in-feature plasma flux (IFPF) within the feature during the etch operation. The methods may further include estimating a quantity characteristic of an edge placement error (EPE) of the feature by comparing the one or more quantities characteristic of the IFPF to those in a look-up table (LUT, and/or through application of a multivariate model trained on the LUT, e.g., constructed through machine learning methods (MLM)) which associates values of the quantity characteristic of EPE with values of the one or more quantities characteristics of the IFPF. Thereafter, the initial design layout may be modified based on at the determined quantity characteristic of EPE.

Photoresist design layout pattern proximity correction through fast edge placement error prediction via a physics-based etch profile modeling framework

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Pleasanton, CA, United States of America

Richard Alan Gottscho