Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks

• URL: http://arxiv.org/abs/2205.08378v1
• Date: Tue, 10 May 2022 23:18:22 GMT
• Title: Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks
• Authors: Angel Yanguas-Gil and Jeffrey W. Elam
• Abstract summary: We introduce a dataset designed to train neural networks to predict saturation times based on the dose time and thickness values measured at different points of the reactor. The results show that trained neural networks can accurately predict saturation times without requiring any prior information on the surface kinetics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we explore the application of deep neural networks to the optimization of atomic layer deposition processes based on thickness values obtained at different points of an ALD reactor. We introduce a dataset designed to train neural networks to predict saturation times based on the dose time and thickness values measured at different points of the reactor for a single experimental condition. We then explore different artificial neural network configurations, including depth (number of hidden layers) and size (number of neurons in each layers) to better understand the size and complexity that neural networks should have to achieve high predictive accuracy. The results obtained show that trained neural networks can accurately predict saturation times without requiring any prior information on the surface kinetics. This provides a viable approach to minimize the number of experiments required to optimize new ALD processes in a known reactor. However, the datasets and training procedure depend on the reactor geometry.

Related papers

• Diffused Redundancy in Pre-trained Representations [98.55546694886819]
  We take a closer look at how features are encoded in pre-trained representations. We find that learned representations in a given layer exhibit a degree of diffuse redundancy. Our findings shed light on the nature of representations learned by pre-trained deep neural networks.
  arXiv  Detail & Related papers  (2023-05-31T21:00:50Z)
• Deep Learning Weight Pruning with RMT-SVD: Increasing Accuracy and Reducing Overfitting [0.0]
  The spectrum of the weight layers of a deep neural network (DNN) can be studied and understood using techniques from random matrix theory (RMT) In this work, these RMT techniques will be used to determine which and how many singular values should be removed from the weight layers of a DNN during training, via singular value decomposition (SVD) We show the results on a simple DNN model trained on MNIST.
  arXiv  Detail & Related papers  (2023-03-15T23:19:45Z)
• Globally Optimal Training of Neural Networks with Threshold Activation Functions [63.03759813952481]
  We study weight decay regularized training problems of deep neural networks with threshold activations. We derive a simplified convex optimization formulation when the dataset can be shattered at a certain layer of the network.
  arXiv  Detail & Related papers  (2023-03-06T18:59:13Z)
• A predictive physics-aware hybrid reduced order model for reacting flows [65.73506571113623]
  A new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems. The number of degrees of freedom is reduced from thousands of temporal points to a few POD modes with their corresponding temporal coefficients. Two different deep learning architectures have been tested to predict the temporal coefficients.
  arXiv  Detail & Related papers  (2023-01-24T08:39:20Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• Variational Inference for Infinitely Deep Neural Networks [0.4061135251278187]
  unbounded depth neural network (UDN) We introduce the unbounded depth neural network (UDN), an infinitely deep probabilistic model that adapts its complexity to the training data. We study the UDN on real and synthetic data.
  arXiv  Detail & Related papers  (2022-09-21T03:54:34Z)
• A Local Geometric Interpretation of Feature Extraction in Deep Feedforward Neural Networks [13.159994710917022]
  In this paper, we present a local geometric analysis to interpret how deep feedforward neural networks extract low-dimensional features from high-dimensional data. Our study shows that, in a local geometric region, the optimal weight in one layer of the neural network and the optimal feature generated by the previous layer comprise a low-rank approximation of a matrix that is determined by the Bayes action of this layer.
  arXiv  Detail & Related papers  (2022-02-09T18:50:00Z)
• Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks [68.8204255655161]
  Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks. We explore the diversity of the neurons within the hidden layer during the learning process. We analyze how the diversity of the neurons affects predictions of the model.
  arXiv  Detail & Related papers  (2021-09-20T15:12:16Z)
• Deep Learning with a Classifier System: Initial Results [0.0]
  This article presents the first results from using a learning classifier system capable of performing adaptive computation with deep neural networks. The system automatically reduces the number of weights and units while maintaining performance after achieving a maximum prediction error.
  arXiv  Detail & Related papers  (2021-03-01T16:40:12Z)
• A Greedy Algorithm for Quantizing Neural Networks [4.683806391173103]
  We propose a new computationally efficient method for quantizing the weights of pre- trained neural networks. Our method deterministically quantizes layers in an iterative fashion with no complicated re-training required.
  arXiv  Detail & Related papers  (2020-10-29T22:53:10Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.