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Thin film processing

Fast-track the next generation of electronic devices

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.

How machine learning enables accurate prediction of precursor volatility

Innovation in atomic-level processing with atomistic simulation and machine learning

Sublime precursors: How modeling organometallics at surfaces drives innovation in materials processing

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Online certification courses

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

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

Online certification course: Level-up your skill set in catalysis modeling Materials Science Materials Science
Molecular modeling for materials science applications: Homogeneous catalysis and reactivity

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

Key Products

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


Integrated graphical user interface for nanoscale quantum mechanical simulations


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

MS Informatics

Efficient machine learning model builder for materials science applications

MS Maestro

Complete modeling environment for your materials discovery


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


Your complete digital molecular design lab

MS Reactivity

Automatic workflow for accurate prediction of reactivity and catalysis

Training Tutorials

Modeling Surfaces
View tutorial
Activation energies for reactivity in solids and on surfaces
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Periodic descriptors for inorganic solids
View tutorial
Beta elimination reactions
View tutorial


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

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

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

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.