# US 2016/0253441 A1 · Fast method for reactor and feature scale coupling in ald and cvd > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2016/0253441 A1 - Document ID: US20160253441A1 - Status: Published application - Kind: A1 - Application number: 14/633,025 - Filed: 2015-02-26 - Published: 2016-09-01 - Earliest claimed: 2015-02-26 - Assignees: Uchicago Argonne, LLC - Applicants: UChicago Argonne, LLC - Inventors: Angel Yanguas-Gil; Jeffrey W. Elam - Web page: https://patentgrep.com/patent/US20160253441A1 ## CPC - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - G16C 10/00: Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like - G16C 99/00: Subject matter not provided for in other groups of this subclass - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G16C: COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE ## Abstract Transport and surface chemistry of certain deposition techniques is modeled. Methods provide a model of the transport inside nanostructures as a single-particle discrete Markov chain process. This approach decouples the complexity of the surface chemistry from the transport model, thus allowing its application under general surface chemistry conditions, including atomic layer deposition (ALD) and chemical vapor deposition (CVD). Methods provide for determination of determine statistical information of the trajectory of individual molecules, such as the average interaction time or the number of wall collisions for molecules entering the nanostructures as well as to track the relative contributions to thin-film growth of different independent reaction pathways at each point of the feature. ## Claims ### Claim 1 A method for modeling to model reaction and transport of gases and liquid comprising: identifying a set of tracking variables that completely define the state of the nanostructured material; identifying a set of output variables that completely define the impact of the nanostructured substrate at the reactor scale; computing, via a processor of a computer, the set of output variables as a function of the set of tracking variables to generate a functional relationship between the set of tracking variables and the set of output variables; storing the functional relationship between the tracking and the output variables both as a data structure in a memory of the computer; solving the reactive transport at a reactor scale of the reacting species and the tracking variables using an iterative method; and determining, via the processor, the value of the output variables from the tracking variables using the functional relationship between them stored in the memory for every discretized region of the reactor-scale model interacting with the nanostructured material and for all iteration steps. ### Claim 2 (depends on 1) The method of claim 1, where the data structure is a structure selected from the group consisting of a lookup table, a hash or dictionary, a tree structure, and a relational database. ### Claim 3 (depends on 1) The method of claim 1, further comprising determining at runtime whether the output variables can be determined by direct lookup or requires solving a feature scale model. ### Claim 4 (depends on 1) The method of claim 1, further comprising compiling a database storing the functional relationship between tracking and output variables that can be used by multiple simulations without having to recompute the feature-scale model. ### Claim 5 (depends on 1) The method of claim 1, further comprising applying a computational fluid dynamic model for solving the reactive transport at the reactive scale of the reacting species and the tracking variables ### Claim 6 (depends on 1) The method of claim 1, wherein the set of output variables is given by the effective sticking probability for each of the reactive molecules being solved at the reactor-scale. ### Claim 7 (depends on 1) The method of claim 1, wherein the feature-scale model comprises: defining a set of transient states corresponding to the reversible interaction of reacting molecules with the surface of the nanostructured materials and defining a set of absorbing states corresponding to the final outcome of the process of either a molecule reacting with the feature or escaping from the surface; defining a set of transition probabilities between transient states and between transient and absorbing states; solving the probabilistic outcome using absorbing Markov Chain formalism. ### Claim 8 A method for simulating reactor-scale ALD, comprising generating a look-up table for feature-scale values; applying a feature-scale model having a plurality of time-steps; for each of the plurality of time steps, identifying a plurality of surface element; for each plurality of surface elements corresponding to each of the plurality of time steps, determining a total precursor exposure and querying the look-up table based upon the determined total precursor exposure to determine a reaction probability; and applying the reaction probability at the reactor-scale. ### Claim 9 (depends on 1, 9) The method of claim 9, wherein the connection between the feature and reactor scale models takes place using the method derived in claim 1 ### Claim 10 (depends on 9) The method of claim 9, wherein generating the look-up table comprises: determining nanostructures for the thin film deposition; modeling transport of a reacting molecule in the thin film deposition as a Markov chain process; determining a probability that a reacting molecule adsorbs. ### Claim 11 A computer implemented system for simulating reactor-scale ALD, comprising: a processor, non-transitory computer-readable memory having instructions thereon, the instructions for: determining nanostructures for the thin film deposition; modeling transport of a reacting molecule in the thin film deposition as a Markov chain process; determining a probability that a reacting molecule adsorbs; and applying the probably to a reactor-scale to simulate a thin film deposition on a reactor scale. ### Claim 12 (depends on 11) The computer implemented system of claim 11, wherein modeling transport includes a plurality of transition states corresponding to adsorption and recombination. ### Claim 13 A method to allow the concurrent solution of the coating of nanostructured materials consisting of: solving a feature-scale model to determine look-up tables for the relevant tracking and output variables for the nanostructured material; storing the results in a database for future retrieval; and concurrently solving the reactor-scale model for a multiplicity of conditions and reactor configurations. ### Claim 14 (depends on 13) The method in claim 13 further comprising optimizing the processing conditions in a physical reactor. ### Claim 15 (depends on 14) The method in claim 14 further comprising optimizing the design of a physical reactor. ### Claim 16 (depends on 13) The method of claim 13 further comprising optimizing design of core-shell structure materials. ### Claim 17 (depends on 16) The method of claim 16, wherein the core-shell structure materials are selected from the group consisting of core-shell nanostructured electrodes, core-shell particles, and the conformal coating with multilayers of high aspect ratio features and recessed substrates.