ALD Reactor Scale Coupling via Markov Chain Lookup Tables
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Solution Overview
Problem
Current multiscale models for simulating the coating of nanostructured materials are computationally expensive and time-consuming, especially for Atomic Layer Deposition (ALD) processes, due to the need for repetitive application of feature-scale models and substantial memory requirements, which hinders the optimization of reactor design and process efficiency in fields like semiconductor processing and energy storage.
Innovation Solution
A method that decouples the feature and reactor scales by identifying tracking and output variables, generating a functional relationship between them, and storing this relationship for efficient computation, allowing concurrent solution of reactor-scale models and enabling the use of look-up tables and Markov chain processes to simulate ALD on nanostructured materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiscale models are applied to simulate coating of nanostructured materials, then the simulation accuracy is improved, but the computational time and complexity increase significantly
Solution Approach 1:
The simulation domain is divided into interconnected regions at reactor scale, and a separate feature-scale model is applied only to regions facing nanostructured substrate. This segmentation allows the complex multiscale simulation to be broken into manageable parts, solving reactor-scale transport and feature-scale transport separately while linking them through interface conditions.
Solution Approach 2:
The feature-scale model is solved in advance to generate look-up tables containing pre-computed transport information. During the main reactor-scale simulation, these pre-computed tables are queried instead of solving the feature-scale model repeatedly, significantly reducing computational time while maintaining accuracy.
2Manufacturing precision
If the feature-scale model is applied repeatedly for each region and iteration, then the coating process is accurately simulated, but the computational cost becomes extremely high
Solution Approach 1:
The feature-scale model is solved beforehand to generate comprehensive look-up tables that capture the essential transport behavior. These tables are then reused across all iterations and regions during reactor-scale simulation, eliminating the need to repeatedly solve the computationally expensive feature-scale model while preserving coating process accuracy.
Solution Approach 2:
Instead of solving the feature-scale model repeatedly, pre-computed look-up tables serve as copies of the essential feature-scale information. These tables are queried during reactor-scale simulation to obtain feature-scale transport effects without the computational overhead of full feature-scale model solutions.
3Measurement precision
If a detailed reactor-scale simulation is performed, then the transport phenomena are accurately captured, but the computational resources required become prohibitive
Solution Approach 1:
The simulation is segmented into reactor-scale and feature-scale components. The reactor-scale model captures transport phenomena in the gas phase and precursor distribution, while the feature-scale model handles transport within nanostructured materials. This segmentation allows each model to be optimized for its specific scale, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The problem is approached by adding a hierarchical dimension, solving at two different length scales (reactor scale and feature scale) rather than attempting a single comprehensive model. This multiscale approach reduces computational resources by applying appropriate levels of detail only where necessary.
Data Source
AI summary
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.