Applied Materials, Inc.

Lam Research Corporation

Atashi Basu

Abhijit Basu Mallick

Srinivas Gandikota

Paul F Ma

David P Thompson

Kelvin Chan

Jeffrey W Anthis

Eswaranand Venkatasubramanian

Rajesh Sathiyanarayanan

Ning Li

Michal Danek

Siddarth A Krishnan

Mihaela A Balseanu

Naomi Yoshida

Ziqing Duan

David M Fried

Lin Dong

Kapil Umesh Sawlani

Nasrin Kazem

Kurt Fredrickson

Srinivas D Nemani

Ellie Y Yieh

Seshadri Ganguli

Pramit Manna

Yixiong Yang

Yu Lei

Anand Chandrashekar

Sang Ho Yu

Wei V Tang

Swaminathan T Srinivasan

Yi Xu

Diwakar N Kedlaya

Shih Chung Chen

Liqi Wu

Khokan Chandra Paul

Wenyu Zhang

Dongyao Li

Srividya Natarajan

Michael Bowes

Drew Phillips

Chen-Han Lin

Kaliappan Muthukumar

Emily Ann Alden

Methods and precursors for selectively depositing a metal film on a silicon nitride surface relative to a silicon oxide surface are described. The substrate comprising both surfaces is exposed to a blocking compound to selectively block the silicon oxide surface. A metal film is then selectively deposited on the silicon nitride surface.

Methods and precursors for selective deposition of metal films

Methods, systems, and computer programs are presented for determining the recipe for manufacturing a semiconductor with the use of machine learning (ML) to accelerate the definition of recipes. One general aspect includes a method that includes an operation for performing experiments for processing a component, each experiment controlled by a recipe, from a set of recipes, that identifies parameters for manufacturing equipment. The method further includes an operation for performing virtual simulations for processing the component, each simulation controlled by one recipe from the set of recipes. An ML model is obtained by training an ML algorithm using experiment results and virtual results from the virtual simulations. The method further includes operations for receiving specifications for a desired processing of the component, and creating, by the ML model, a new recipe for processing the component based on the specifications.

Determination of recipes for manufacturing semiconductor devices

A microelectronic device on a semiconductor substrate comprises: a gate electrode; and a spacer adjacent to the gate electrode, the spacer comprising: a the low-k dielectric film comprising one or more species of vanadium oxide, which is optionally doped, and an optional silicon nitride or oxide film. Methods comprise depositing a low-k dielectric film optionally sandwiched by a silicon nitride or oxide film to form a spacer adjacent to a gate electrode of a microelectronic device on a semiconductor substrate, wherein the low-k dielectric film comprises a vanadium-containing film.

Doped and undoped vanadium oxides for low-k spacer applications

Metal coordination complexes comprising at least one diazabutadiene based ligand having a structure represented by: where Rand Rare selected from the group consisting of C4-C10 alkyl groups; and Rand Rare each independently selected from the group consisting of H, C1-C6 alkyl, cycloalkyl, or aryl groups and the difference in the number of carbons in Rand Ris greater than or equal to 2. Processing methods using the metal coordination complexes are also described.

Metal precursors with modified diazabutadiene ligands for CVD and ALD and methods of use

Metal coordination complexes comprising at least one diazabutadiene based ligand having a structure represented by: 

Precursors for the atomic layer deposition of transition metals and methods of use

Methods of modifying the threshold voltage of metal oxide stacks are discussed. These methods utilize materials which provide larger shifts in threshold voltage while also being annealed at lower temperatures.

Methods and materials for modifying the threshold voltage of metal oxide stacks

Provided herein are systems and methods for optimizing feature fill processes. The feature fill optimization systems and methods may be used to optimize feature fill from a small number of patterned wafer tests. The systems and methods may be used for optimizing enhanced feature fill processes including those that include inhibition and/or etch operations along with deposition operations. Results from experiments may be used to calibrate a feature scale behavioral model. Once calibrated, parameter space may be iteratively explored to optimize the process.

Fill process optimization using feature scale modeling

Methods for depositing silicon nitride films with higher nitrogen content are described. Certain methods comprise exposing a substrate to a silicon-nitrogen precursor and ammonia plasma to form a flowable polymer, and then curing the polymer to form a silicon nitride film. Certain methods cure the flowable polymer without the use of a UV-cure process. Also described is the film generated by the methods described above.

Silicon nitride films with high nitrogen content

Methods comprising depositing a film material to form an initial film in a trench in a substrate surface are described. The film is treated to expand the film to grow beyond the substrate surface.

Deposition and treatment of films for patterning

Film stacks and methods of forming film stacks including a high-k dielectric layer on a substrate, a high-k capping layer on the high-k dielectric layer, an n-metal layer on the high-k capping layer and an n-metal capping layer on the n-metal layer. The n-metal layer having an aluminum rich interface adjacent the high-k capping layer.

Low thickness dependent work-function nMOS integration for metal gate

Methods of depositing a film selectively onto a first substrate surface relative to a second substrate surface are described. The methods include depositing a second metal on a first metal without protecting the dielectric, protecting the metal with a cross-linked self-assembled monolayer and depositing a second dielectric on the first dielectric while the metal is protected.

Schemes for selective deposition for patterning applications

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Menlo Park, CA, United States of America

Atashi Basu