APPLICATION NOTE

Innovation in
atomic-levelprocessing
with atomistic
simulation andmachine learning
Many of today’s high-tech devices are manufactured by processing materials
at the nanoscale, including computer chips, data storage and communications
devices, sensors, solar cells and batteries. Making devices smaller, more
powerful and more energy-efficient means developing new patterning,deposition and etch techniques at ever-finer resolution, in some cases down tojust a few atoms thick. Chemical processing is being pushed to its limits in termsof purity, uniformity, conformality and substrate-selectivity.
Optimizing a deposition or etch process to more stringent tolerances requires a
deep understanding of the underlying chemistry. Integrating new materials intoa device often requires the discovery of new chemicals with improved properties.
In both respects, researchers are increasingly turning to computer simulations to
cut down on lab work and make discoveries faster.
The Schrödinger Materials Science Platform offers computational tools
specifically tailored to tackling this problem, by studying the gas-surface
chemistries of deposition, etch and related nanofabrication processes.
Schrödinger software is designed for rapid and automated enumeration ofchemical space, detailed study of gas-surface chemistries at the quantum
mechanical level (with density functional theory, DFT) and prediction of key
properties. This modeling approach can both deepen our understanding ofexisting processes and help discover novel chemicals.

Automate Precursor
Screening for
Improved Properties

atoms to form gaseous products. Lists of candidate
precursor molecules can be efficiently screened
against deposition or etch chemistries using Jaguar,
Schrödinger’s code for molecular DFT. Figure 1 displays
the computed reactivity of the complexes with respect
to Pd deposition.

Organometallic complexes can be used to deposit or etchpure metals or compounds such as oxides or nitrides.
However, synthesis of the complexes in the lab can betime-consuming and difficult because many complexes
react violently in air. Subsequent characterization andtesting in a reactor is even more costly. Digital chemistry
can rapidly screen larger numbers of chemicals thancan ever be experimentally tested, narrowing down thechemical space and discovering the most promising
candidate molecules for deposition or etch.

Volatility and Thermal Stability

Schrödinger provides easy-to-use software for automatedgeneration and assessment of hundreds or thousands ofprecursor gases. A library of hundreds of commonly-used
ligands is provided, including bidentate and haptic ligands,
and the library can be customized. A selection from thelibrary is then used to enumerate and build all possible
precursor complexes. We present here a case study inwhich a library of 10 ligands form 55 distinct complexes
with divalent palladium (Pd) as the metal cation and someof these candidate complexes are shown in Figure 1.

Synthesizability
Combining two or more different ligands in a heteroleptic
complex is a promising way to tune precursor properties, but
it is difficult to guess whether a given combination can in fact
be synthesized. This question can be answered by computing
the energy for ligand exchange back to homoleptic complexes.
In the Pd example, this reveals that 14 of the heteroleptic
complexes are unstable and can not be synthesized.

Reactivity
Precursors for deposition must be carefully designed
so as to react cleanly and deposit the desired solidmaterial, via thermolysis or plasma activation(chemical vapor deposition, CVD) or via reactions withadsorbed fragments (atomic layer deposition, ALD).
Likewise, reagents for etching must react with surface

To be useful in a real CVD or ALD process, a precursor
must also be volatile and thermally stable during storage
and delivery. This limits the process to a window of viabletemperatures, with insufficient vapor pressure at lower
temperatures and impurities from decomposition at
higher temperatures. For instance, Pd(thd)2 suffers fromdecomposition when heated to 200-250°C for the ALDof Pd metal.1,2 Calculating volatility and thermal stability
with Schrödinger’s tools gives information on how to tune
these properties and widen the temperature window for
maximum process flexibility.
Schrödinger has used machine learning (ML) to develop
a unique model for predicting the evaporation orsublimation temperature of organometallic complexes
at a given pressure. The ML-volatility model is accurate
to an average of ±9°C over the training set and canevaluate hundreds of molecules per second.
The thermal stability of precursor gases can be quantified
by considering the unimolecular decomposition channels:
bond dissociation and β-H elimination. All such reactions
in each molecule are automatically computed at the DFTlevel and the lowest energy reaction is selected as themost likely. The minimum decomposition energy of eachcomplex is thus a measure of its thermal stability.
These approaches were applied to compute the propertiesof the Pd complexes, and Figure 1 shows how this allows
the candidate precursors to be assessed.
Rational innovation is facilitated by Schrödinger’s atomistic
modeling tools. Novelty is maximized by using a large
library of ligands to build a wide variety of complexes,homoleptic and heteroleptic. Risk is minimized by selecting
for lab study only those precursors computed to properly
combine the desired properties of synthesizability,volatility, reactivity and stability.

Figure 1. Screening novel divalent
Pd precursors for deposition. Threesample precursor structures areshown on the left. The x-axis shows
ML-predicted volatility and the y-axis
shows thermal stability. The area of thecircles corresponds to the DFT-computed
reactivity towards deposition of Pdmetal. The most volatile and stablecomplexes are towards the top left ofthe graph.

2Gain Insights Into
Competing Processes
for Optimum
Deposition Conditions
A deposition or etch process consists of a sequence ofindividual reaction steps, often in competition with otherreactions. Simulations give atomic-level insight into thesecomplicated mechanisms and how they depend on reactor
conditions such as temperature and pressure. Here we
investigate the mechanism of gold deposition from thesingle-source precursor trimethylphosphinotrimethylgold(III).
Experiments show that gold films grow in a selflimiting fashion when this precursor is pulsed at low
temperature, switching over to continuous CVD at
100°C and higher, but the reason is unknown.3
To explain this behavior, the Quantum ESPRESSO codefor periodic DFT has been used to examine the detailed
mechanism of reactive adsorption of the precursor onto
a metallic gold surface. Thermodynamics of competing
reaction steps have been computed as a function ofprocess temperature and pressure with Schrödinger’s
tools for adsorption and surface chemistry.4The results are summarized in Figure 2. The physisorbed
precursor is in a metastable state, from which dissociation ofAu-P or Au-C bonds can lead to chemisorption.

Au-C dissociation and elimination of C2H6 isfound to be the favored pathway, due to astrong thermodynamic driving force towards
reduction of Au(III) to Au(I) and eventually Au(0).
Trimethylphosphine is computed to bind to thesurface, forming an adlayer that passivates against
further precursor adsorption. This explains thelow rate of self-limited deposition at <100°C. Thecomputed free energies also reveal that desorptionof trimethylphosphine becomes favored at higher
temperatures, in agreement with the crossoverto CVD seen experimentally at >100°C.
Finding out which bond breaks and how thesurface is passivated opens up chemically-informed
strategies for improving the process, for example
by choosing a suitable co-reagent or modifying
the precursor structure. Digital chemistry allows
rational design to take the place of trial-and-error.
This example illustrates how changes in process
behavior can be traced back to competing chemicalpathways. DFT simulations give unique qualitative
insight into atomic structure, bonding and oxidation
states of the intermediates along each reaction
pathway. In addition, quantitative information about
kinetics and thermodynamics as a function of processtemperature can be computed, giving valuableguidance on how to optimize the nanofabrication
process. The result is improved control of purity,uniformity, phase and substrate-selectivity.

Figure 2. Mechanism of deposition of gold from trimethylphosphinotrimethylgold(III) computed with DFT, including free energies evaluated at 100°C.

3Summary
These cases illustrate how Schrödinger’s Materials Science Platform provides powerful andcustomizable computational solutions to accurately model surface reactivity, predict precursor
properties and discover novel chemicals for optimized materials deposition or etch processes.

References1.

Low temperature atomic layer deposition of noble metals using ozone and molecular hydrogen as reactants,
J. Hämäläinen et al., Thin Solid Films 2013, 531, 243-250, DOI 10.1016/j.tsf.2013.01.091.

2.

Atomic layer deposition of noble metals: Exploration of the low limit of the deposition temperature,
T. Aaltonen et al., J. Mater. Res. 2004, 19, 3353–3358, DOI 10.1557/JMR.2004.0426.

3.

Reaction mechanism of the Me3AuPMe3–H2 plasma-enhanced ALD process, M. Van Daele et al.,Phys. Chem. Chem. Phys. 2020, 22, 11903-11914, DOI 10.1039/C9CP06855D.

4.

First-principles investigation of crossover between ALD and CVD in the thin film deposition of gold,
C. N. Brock. D. J. Giesen, A. Fonari & S. D. Elliott, Materials Research Society Spring Meeting, 2022.

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