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Thin Film Processing

Fast-track the next generation of electronic devices

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Thin film processing

Optimize semiconductor processing with the power of digital simulations

Scientists in the semiconductor industry are under constant pressure to deliver electronic devices that are smaller, more powerful, and more energy-efficient. Fabricating the next generation of device structures at the micro- or nano-scale is a huge and growing challenge.

Schrödinger’s Materials Science platform offers advanced computational tools to help companies optimize atomic-level processing for electronics and other high-tech industries, and improve device performance.

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Intuitive computational workflows designed by experts in thin film processing

Easy-to-use system builders for all material types
Powerful workflows for molecular simulation, machine learning, and data analysis
Dedicated customer support and extensive training resources

Solutions for the material and process challenges facing today’s electronics industry

Optimize precursor materials

  • Compute key physical and chemical properties including volatility and thermal stability of precursors for thin film deposition or etch
  • Gain insights in silico into the chemistry at the surface, where precursors react with substrates
  • Speed up new precursor discovery with large-scale screening and machine learning

Refine semiconductor fabrication

  • Identify the best precursor and co-reagent combinations for wafer processing by MOCVD, ALD, and etch
  • Predict surface reactivity to find the optimum windows for experimental process parameters
  • Gain atomic-level insights to help troubleshoot processes

Simulate materials for more predictable chip and device manufacturing

  • Simulate materials properties that complement metrology and provide insights into device performance
  • Identify root causes of device failure by investigating materials at the atomic level
  • Test and screen new material combinations in silico

Case studies & webinars

Discover how Schrödinger technology is being used to solve real-world research challenges.

Materials Science Webinar

Digital forum on atomic layer deposition: Bridging theory and experiment to design a process for silicon carbonitride

Join us as we discuss how effectively theory and experiment are working together to solve the R&D challenges facing high-tech industries.

Materials Science Webinar

Accelerating materials discovery with physics-informed AI/ML

This webinar series will explore how cutting-edge computational methods are revolutionizing the design and optimization of pharmaceutical drugs, biologics , and advanced materials.

Materials Science Webinar

Advancing machine learning force fields for materials science applications

In this webinar, we will introduce Schrödinger’s state-of-the-art MLFF architecture, called Message Passing Network with Iterative Charge Equilibration (MPNICE), which incorporates explicit electrostatics for accurate charge representations.

Materials Science Webinar

Accelerating chemical innovation with AI/ML: Breakthroughs across materials applications

In this webinar, we will explore how AI/ML is driving impactful advancements in materials innovation, highlighting case studies that illustrate cutting-edge ML techniques in diverse applications.

Materials Science Webinar

High-performance materials discovery: A decade of cloud-enabled breakthroughs

This talk will showcase how Schrödinger’s integrated materials science platform enables massive parallel screening and de novo design campaigns across diverse applications.

Materials Science Webinar

Purposeful simulation: Maximising impact in surface chemistry modelling

In this webinar, learn about a variety of atomistic models of surfaces and gain perspective on the underlying rationale, benefits and limitations of each.

Materials Science Webinar

Accelerating the Design of Asymmetric Catalysts with Schrödinger’s Digital Chemistry Platform

In this webinar, we demonstrate how Schrödinger’s advanced digital chemistry platform can be used to accelerate the direct design and discovery of asymmetric catalysts.

Materials Science Webinar

Efficient Computation of Process Parameters for Controlling the Chemistry of Deposition or Etch

In this webinar, we illustrate how atomic-scale DFT can be embedded into higher-level computational schemes for accurate and achievable prediction of the conditions and parameters for controlling chemical processes.

Materials Science Webinar

AI/ML meets physics-based simulations: A new era in complex materials design

In this webinar, we demonstrate the application of this combined approach in designing materials and formulations across diverse materials science applications, from battery electrolytes and fuel mixtures to thermoplastics and OLED devices. 

Materials Science Webinar

Webinar Series: From Molecules to Materials Applications

In this webinar series, we present molecular modeling techniques and their transformative impact on Materials Science research using the Schrödinger Materials Science tools.

Online certification courses

Molecular modeling for materials science applications Materials Science Materials Science
Surface chemistry

Molecular quantum mechanics, periodic quantum mechanics, and machine learning approaches for studying atomic layer processing and heterogeneous catalysis

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Online certification course: Level-up your skill set in catalysis modeling Materials Science Materials Science
Homogeneous catalysis & reactivity

Molecular quantum mechanics and machine learning approaches for studying reactivity and mechanism at the molecular level

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Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Materials Science Tutorial

Locating Adsorption Sites on Surfaces

Learn how to locate adsorption sites on surfaces.

Materials Science Documentation

Machine Learning Force Fields

Machine Learning Force Fields (MLFFs) offer a novel approach for predicting the energies of arbitrary systems.

Materials Science Documentation

MS Surface

A solution for heterogeneous catalysis and materials processing.

Materials Science Documentation

MS Reactivity

Automated workflows for design, optimization, and unsupervised mechanism discovery in molecular chemistry.

Materials Science Documentation

MS Informatics

Automated machine learning tools for materials science applications

Materials Science Documentation

MS Microkinetics

An efficient tool for surface reaction kinetics.

Materials Science Documentation

Quantum ESPRESSO Interface

A comprehensive graphical user interface for calculation set-up, job control and results analysis.

Materials Science Tutorial

Applied Machine Learning for Formulations

Learn to apply the Formulation Machine Learning Panel across a range of materials applications. This tutorial assumes that you have already completed the Machine Learning for Formulations tutorial.

Materials Science Tutorial

Machine Learning for OLED Device Design

Learn to train a machine learning model to predict properties of OLED devices and subsequently apply this trained model to predict target properties for new OLED devices unseen during training.

Materials Science Documentation

Materials Science Documentation

Comprehensive reference documentation covering materials science panels and workflows.

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Key Products

Learn more about the key computational technologies available to progress your research projects.

MS Surface

Solution for heterogeneous catalysis and materials processing

Virtual Cluster

Secure, scalable environment for running simulations on the cloud

Quantum ESPRESSO Interface

Integrated graphical user interface for nanoscale quantum mechanical simulations

Jaguar

Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties

MS Informatics

Automated machine learning tools for materials science applications

MS Maestro

Complete modeling environment for your materials discovery

DeepAutoQSAR

Automated, scalable solution for the training and application of predictive machine learning models

LiveDesign

Your complete digital molecular design lab

MS Reactivity

Automated workflows for design, optimization, and unsupervised mechanism discovery in molecular chemistry

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Thermal Atomic Layer Etching of Aluminum Oxide (Al2O3) Using Sequential Exposures of Niobium Pentafluoride (NbF5) and Carbon Tetrachloride (CCl4): A Combined Experimental and Density Functional Theory Study of the Etch Mechanism

Sharma V. et al. Chem. Mater. 2021, 33, 8, 2883–2893

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Quantifying the Extent of Ligand Incorporation and the Effect on Properties of TiO2 Thin Films Grown by Atomic Layer Deposition Using an Alkoxide or an Alkylamide

Dufond M.E et al. Chem. Mater. 2020, 32, 4, 1393–1407

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Atomic Layer Deposition of Localized Boron- and Hydrogen-Doped Aluminum Oxide Using Trimethyl Borate as a Dopant Precursor

Mattelaer F. et al. Chem. Mater. 2020, 32, 10, 4152–4165

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Software & services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.