Lam Research Corporation

Vahid Vahedi

Harmeet Singh

Richard Alan Gottscho

David J Cooperberg

John Edward Daugherty

Thorsten B Lill

Alan Jeffrey Miller

Joydeep Guha

Alex Paterson

Saurabh J Ullal

Aaron Eppler

Andrew D Bailey, Iii

Neil M Benjamin

Thomas W Mountsier

Sirish K Reddy

Kaushik Chattopadhyay

Chris Lee

Richard Stephan Wise

Keith William Gaff

Keith Laurence Comendant

Saravanapriyan Sriraman

Jaroslaw Walery Winniczek

Frank Lin

Gowri Kamarthy

Monica Titus

Yoko Yamaguchi

Anthony L Chen

Linda B Braly

Helene Del Puppo

Thomas A Kamp

Yosias Melaku

Andreas Fischer

Peter K Loewenhardt

Andras Kuthi

Meihua Shen

Gary Bultman

Thomas E Wicker

Tom A Kamp

Albert R Ellingboe

Erik A Edelberg

Sanket Sant

Stanley Chan Siu

Andrew J Perry

Raphael Casaes

Jeff A Bogart

Shang-I Chou

Candi Kristoffersen

Albert J Lamm

David T Hodul

Russell F Jewett

Seetharaman Ramachandran

Douglas Ratto

Gregory A Roche

Steve Lee

Rangesh Raghavan

Maria E Barone

Bert Ellingboe

Andy Kuthi

David Cooperberg

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

A tunable multi-zone injection system for a plasma processing system for plasma processing of substrates such as semiconductor wafers. The injector can include an on-axis outlet supplying process gas at a first flow rate to a central zone and off-axis outlets supplying the same process gas at a second flow rate to an annular zone surrounding the central zone. The arrangement permits modification of gas delivery to meet the needs of a particular processing regime by allowing independent adjustment of the gas flow to multiple zones in the chamber. In addition, compared to consumable showerhead arrangements, a removably mounted gas injector can be replaced more easily and economically.

Tunable multi-zone gas injection system

A method for etching features in an OMOM stack with first layer of silicon oxide, a second layer of a metal containing material over the first layer, a third layer of silicon oxide over the second layer, and a fourth layer of a metal containing material over the third layer is provided. A hardmask is formed over the stack. The hardmask is patterned. The OMOM stack is etched through the hardmask.

High aspect ratio etch of oxide metal oxide metal stack

The embodiments disclosed herein pertain to improved methods and apparatus for etching a semiconductor substrate. A plasma grid assembly is positioned in a reaction chamber to divide the chamber into upper and lower sub-chambers. The plasma grid assembly may include one or more plasma grids having slots of a particular aspect ratio, which allow certain species to pass through from the upper sub-chamber to the lower sub-chamber. Where multiple plasma grids are used, one or more of the grids may be movable, allowing for tenability of the plasma conditions in at least the lower sub-chamber. In some cases, an electron-ion plasma is generated in the upper sub-chamber. Electrons that make it through the grid to the lower sub-chamber are cooled as they pass through. In some cases, this results in an ion-ion plasma in the lower sub-chamber.

Internal plasma grid for semiconductor fabrication

A plasma etching system having a substrate support assembly with multiple independently controllable heater zones. The plasma etching system is configured to control etching temperature of predetermined locations so that pre-etch and/or post-etch non-uniformity of critical device parameters can be compensated for.

Adjusting substrate temperature to improve CD uniformity

Methods and systems for controlling processing state of a plasma reactor to initiate processing of production substrates and/or to determine a ready state of a reactor after the reactor has been cleaned and needs to be seasoned for subsequent production wafer processing are provided. The method initiate processing of a substrate in the plasma reactor using settings for tuning knobs of the plasma reactor that are approximated to achieve desired processing state values. A plurality of data streams are received from the plasma reactor during the processing of the substrate. The plurality of data streams are used to identify current processing state values. The method includes generating a compensation vector that identifies differences between the current processing state values and the desired processing state values. The generation of the compensation vector uses machine learning to improve and refile the identification and amount of compensation needed, as identified in the compensation vector. The method further includes transforming the compensation vector into adjustments to the settings for the tuning knobs and then applying the adjustment to the tuning knobs of the plasma reactor.

Method and process of implementing machine learning in complex multivariate wafer processing equipment

An EFEM useful for transferring wafers to and from wafer processing modules comprises an enclosure having a controlled environment therein bounded by a front wall, a back wall, first and second side walls, a top wall, and a bottom wall. The first side wall and the second side wall include two or more wafer load ports wherein each wafer load port is adapted to receive a FOUP. The front wall includes wafer ports configured to attach to respective load locks operable to allow a wafer to be transferred to a front wall cluster processing tool. The back wall includes a wafer port adapted to be in operational relationship with a back wall cluster processing tool. A robot in the EFEM enclosure is operable to transfer wafers through the wafer load ports, the first front wall wafer port, the second front wall wafer port, and the back wall wafer port.

Equipment front end module for transferring wafers and method of transferring wafers

A method for etching features in a stack is provided. A combination hardmask is formed by forming a first hardmask layer comprising carbon or silicon oxide over the stack, forming a second hardmask layer comprising metal over the first hardmask layer, and patterning the first and second hardmask layers. The stack is etched through the combination hardmask.

High aspect ratio etch with combination mask

A wafer is provided into an entrance load lock chamber. A vacuum is created in the entrance load lock chamber. The wafer is transported to a processing tool. The wafer is processed in a process chamber to provide a processed wafer, wherein the processing forms halogen residue. A degas step is provided in the process chamber after processing the wafer. The processed wafer is transferred into a degas chamber. The processed wafer is treated in the degas chamber with UV light and a flow of gas comprising at least one of ozone, oxygen, or HO. The flow of gas is stopped. The UV light is stopped. The processed wafer is removed from the degas chamber.

Method and apparatus of halogen removal

A method of etching a semiconductor substrate with improved critical dimension uniformity comprises supporting a semiconductor substrate on a substrate support in an inductively coupled plasma etch chamber; supplying a first etch gas to a central region over the semiconductor substrate; supplying a second gas comprising at least one silicon containing gas to a peripheral region over the semiconductor substrate surrounding the central region, wherein a concentration of silicon in the second gas is greater than a concentration of silicon in the first etch gas; generating plasma from the first etch gas and second gas; and plasma etching an exposed surface of the semiconductor substrate.

Edge gas injection for critical dimension uniformity improvement

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Albany, CA, United States of America

Vahid Vahedi