Device Simulations

Home / Capabilities / Deposition / Materials and Device Simulations at IMI

Materials and Device Simulations at IMI

The Materials and Device Simulations group at IMI supports materials innovation using state-of-the-art modelling at multiple levels of complexity and scale. DFT simulations start from first principles and provide vital information about the key material properties and atomic structure. This information is combined with empirical data for further thermodynamic and device-level modeling, using both commercial and in-house tools. IMI leverages several approaches as described below.

Phase diagrams, phase stability, and compatibility

Assessing thermodynamic stability is vital to the successful development of new materials and advanced multi-layer stacks. Unintended phase transformations and chemical reactions may physically degrade the device. They can also deteriorate the performance on a more subtle level by changing the chemical potentials inside the stack, which in some materials may increase the number of unwanted point defects (e.g., degrade electrical characteristics). In other materials, they may reduce the density of desirable defects (e.g., affecting the mechanical properties).
Identifying new thermodynamically stable compositions in multicomponent alloys[1] can sometimes be a key step in the development of better new alloy materials. To guide experimentation, IMI performs a thermodynamic analysis beginning with computational assessment of phase diagrams, which can use a number of complementary state-of-the-art methods:

  • CALPHAD modeling relies on commercial databases of assessed free energies of many known materials.
  • DFT-based phase diagrams are constructed when CALPHAD assessments are not available (or deemed compromised) for a particular chemical system. Depending on the desired level of accuracy and complexity, they can be constructed by combining data obtained with different computational approaches, to account for the effects at elevated temperatures:
    • DFT simulations results for T=0K formation energies, including those for special quasirandom structures (SQSs), obtained either internally at IMI [1] or by querying external public databases
    • DFT-based Cluster Expansion (CE) and related methods
    • Phonon calculations
    • ab initio molecular dynamics (AIMD)
  • Phenomenological Selection Rules (PSRs) and machine-learning techniques allow extremely rapid and moderately accurate estimation of phase diagrams within the classes of materials for which they have been developed or “trained”.

High-throughput materials selection and optimization workflow is built on top of this analysis. A large number of potential stack materials can be scanned, for instance identifying candidates that combine specific other properties (e.g., band gap exceeding a certain value) with chemical compatibility, with the neighboring materials in the stack. Alternatively, material combinations pinning the chemical potentials at target values can be selected for defect optimization.

Search for Better Materials & Materials Optimization

First principles calculations can quickly identify potential materials and alloys expected to possess target properties. Typical analysis may involve the following calculations.

  • Energetics of various phases.
  • Densities of states, band gaps and defect levels, and band structures.
  • Kinetic barriers / activation energies for particular transformation pathways.
  • High- or low-frequency dielectric constants, and optical constants.
  • Structural motifs in complex (likely amorphous) materials, as established by ab initio molecular dynamics (AIMD) or atomic structure prediction methods.

Several types of calculations, further combined with thermodynamic or other analysis, are typically required (see the Case Study example). This can serve the following purposes.

  • Search of new materials: scanning material classes and chemical systems that have not yet been used for a particular application (this scan can be further assisted by simpler phenomenological models pre-selecting potential materials for a more detailed DFT analysis).
  • Materials optimization: quantifying the effect of small changes (e.g., “doping”) on the properties of known materials, and using it to optimize the performance (see the Case Study example).

Electrical Performance of Thin Film Devices: Switching, Leakage, and Dielectric Breakdown

An understanding of the mechanisms underlying the strongly nonlinear I-V characteristics of advanced materials for the electronics industry, helps to accelerate and de-risk materials innovation. This modeling includes the following.

  • Coherent account for various leakage mechanisms, including trap-assisted tunneling (TAT).
  • Self-consistent determination of charge states of charge centers (ions and defects, fixed or mobile) and the resulting electric field distribution.
  • Ion motion and defect generation in strong electric fields.
  • Account for the electric signatures of ferroelectric and antiferroelectric switching.
  • Modeling of tunneling via NEGF and complex band structure methods.

Interfaces and Multilayer Materials

The key to a successful development of thin film materials is understanding and harnessing the complex physical phenomena arising at the interfaces. Typical areas of focus include the following.

  • Epitaxial matching: identifying substrate materials that promote growth of a desired phase.
  • DFT simulations of atomic structure, reconstruction, and defect energetics at the interfaces.
  • Fermi level pinning and development of surface dipoles, and the resulting barrier heights that affect leakage in dielectric stacks.
  • Simulations of the phenomena affecting material deposition (e.g., ALD or PVD), wetting, etc.

device_simulations_img3

Defect Thermodynamics

Combining the data for the formation energies of defects in different charge states with the data on the electrode effective work functions and the chemical potentials set by the stack chemistry and the processing conditions identifies possible routes for further stack optimization.

device_simulations_img4

 

Other Simulations and Research

To meet the needs of a specific project, IMI performs other types of simulations, including the following.

  • ANSYS Mechanical and Fluent modeling to custom-design devices and process modules, or to evaluate device performance.
  • Modeling of optical properties of multilayer stacks for anti-reflective coatings, etc.

In addition to performing our own methodological research for some of the transport simulations, IMI collaborates with academic researchers in both method development and applications [2,3]. This allows us to increase the reliability of our simulations beyond that of some of the leading academic simulations groups.

References

[1] High Throughput Computer-Aided DiscoveryOf New Metallic Alloys,IMI-Exabyte.io white paper, https://exabyte.docsend.com/view/syitiek

[2] S.V. Barabash, Ab-initio Perspective on Minimizing Tunneling in Materials for Semiconductor Industry, QuantumHagen workshop, Copenhagen, Denmark, 7/2/2014, https://www.quantumwise.com/conference2014/abstracts.html#barabash

[3] S.V. Barabash, `Phantom” Modes in \textit{Ab Initio} Tunneling Calculations: Implications for Theoretical Materials Optimization, Tunneling, and Transport

http://meetings.aps.org/Meeting/MAR15/Session/M24.7