Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review | Paper Citation Record

Observation 326d39fb-7726-4fc1-90d4-12e4e6b1e12a · outbound

This paper cites N., 2012.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review N., 2012

Reference 1

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Observation df8d66cc-d8f4-4275-a6dc-3b0e743779fc · outbound

This paper cites N., 2012, page 88, Chapter 3.1.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review N., 2012, page 88, Chapter 3.1

Reference 2

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Observation 84e2701a-e9bc-4d91-be34-9b18457c287c · outbound

This paper cites N., 2012, page 135, Chapter 3.3.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review N., 2012, page 135, Chapter 3.3

Reference 3

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This paper cites Handbook of thin film deposition.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Handbook of thin film deposition

Reference 4

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This paper cites F., Butler, S.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review F., Butler, S

Reference 5

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This paper cites What is machine learning?.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review What is machine learning?

Reference 6

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This paper cites D., 2019.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review D., 2019

Reference 7

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Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Unresolved cited work

Reference 8

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Observation 59bf0381-29ea-4b42-ad90-807fbdca7c7b · outbound

This paper cites Physics- informed machine learning.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Physics- informed machine learning

Reference 9

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This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems in- volving nonlinear partial differential equations.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Physics-informed neural networks: A deep learning framework for solving forward and inverse problems in- volving nonlinear partial differential equations

Reference 10

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This paper cites Introduction to semiconductor manufactur- ing technology.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Introduction to semiconductor manufactur- ing technology

Reference 11

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This paper cites Moore’s law.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Moore’s law

Reference 12

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This paper cites Euv lithography.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Euv lithography

Reference 13

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This paper cites Introduction to fin- fet: Formation process, strengths, and future exploration.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Introduction to fin- fet: Formation process, strengths, and future exploration

Reference 14

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This paper cites Finfet ver- sus gate-all-around nanowire fet: Performance, scaling, and variability.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Finfet ver- sus gate-all-around nanowire fet: Performance, scaling, and variability

Reference 15

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This paper cites High-k/metal gate innovations en- abling continued cmos scaling.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review High-k/metal gate innovations en- abling continued cmos scaling

Reference 16

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This paper cites Recent advances and trends in advanced packaging.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Recent advances and trends in advanced packaging

Reference 17

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This paper cites Chapter 12 - structure and prop- erties of films.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Chapter 12 - structure and prop- erties of films

Reference 18

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Observation e7b77a75-eb4b-403a-8ef3-19a464a19c41 · outbound

This paper cites E., 2000.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review E., 2000

Reference 19

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Observation 5068aa08-2ace-4133-b427-344981100d76 · outbound

This paper cites Chemical methods of thin film de- position: Chemical vapor deposition, atomic layer depo- sition, and related technologies.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Chemical methods of thin film de- position: Chemical vapor deposition, atomic layer depo- sition, and related technologies

Reference 20

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Observation 54ccd40e-8782-4b28-82f9-c9c3c9c1665f · outbound

This paper cites Atomic layer deposi- tion and other thin film deposition techniques: from princi- ples to film properties.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Atomic layer deposi- tion and other thin film deposition techniques: from princi- ples to film properties

Reference 21

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Observation 9a9f80de-9ea0-4ed1-acba-d4f64c4fe5cc · outbound

This paper cites Chapter 3 - an overview of deep learning in big data, image, and signal processing in the modern digital age.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Chapter 3 - an overview of deep learning in big data, image, and signal processing in the modern digital age

Reference 22

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Observation 9df47f6b-eaf2-4a9c-ba96-758f77293c95 · outbound

This paper cites A comprehensive literature review of the applications of ai techniques through the lifecycle of indus- trial equipment.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review A comprehensive literature review of the applications of ai techniques through the lifecycle of indus- trial equipment

Reference 23

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This paper cites Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.592780Z

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This paper cites Deepsem- net: Enhancing sem defect analysis in semiconductor manufacturing with a dual-branch cnn-transformer archi- tecture.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Deepsem- net: Enhancing sem defect analysis in semiconductor manufacturing with a dual-branch cnn-transformer archi- tecture

Reference 25

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Observation e200259c-ff7e-4fdb-b689-267e4ad91dfe · outbound

This paper cites Image-driven machine learning for automatic characterization of grain size and distribution in smart vanadium dioxide thin films.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Image-driven machine learning for automatic characterization of grain size and distribution in smart vanadium dioxide thin films

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.569730Z

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Observation cd4f7316-7f1e-4410-937d-c852285e1149 · outbound

This paper cites Deep transfer wasserstein adversarial network for wafer map defect recognition.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Deep transfer wasserstein adversarial network for wafer map defect recognition

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.558999Z

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Observation fe9c432b-da5d-42d7-a2e7-03be2a9c5388 · outbound

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Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Unresolved cited work

Reference 28

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This paper cites Humana Press, Totowa, NJ, pp.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Humana Press, Totowa, NJ, pp

Reference 29

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This paper cites Machine learning-based model- ing and operation for ald of sio2 thin-films using data from a multiscale cfd simulation.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Machine learning-based model- ing and operation for ald of sio2 thin-films using data from a multiscale cfd simulation

Reference 30

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Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Unresolved cited work

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Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Unresolved cited work

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This paper cites Deep reinforcement learning: A brief survey.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Deep reinforcement learning: A brief survey

Reference 34

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This paper cites Exploring machine learning for semiconductor process optimization: A sys- tematic review.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Exploring machine learning for semiconductor process optimization: A sys- tematic review

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This paper cites Artifi- cial neural network discrimination for parameter estimation and optimal product design of thin films manufactured by chemical vapor deposition.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Artifi- cial neural network discrimination for parameter estimation and optimal product design of thin films manufactured by chemical vapor deposition

Reference 36

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Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Unresolved cited work

Reference 37

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This paper cites Optimizing the chemical vapor deposition process of 4h–sic epitaxial layer growth with machine-learning-assisted multiphysics simulations.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Optimizing the chemical vapor deposition process of 4h–sic epitaxial layer growth with machine-learning-assisted multiphysics simulations

Reference 38

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This paper cites When magnetron sput- tering deposition meets machine learning: Application to process anomaly detection.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review When magnetron sput- tering deposition meets machine learning: Application to process anomaly detection

Reference 39

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This paper cites A contextual sensor system for non-intrusive machine status and energy monitoring.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review A contextual sensor system for non-intrusive machine status and energy monitoring

Reference 40

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Observation 4e2f9df8-4547-4903-9f05-6d105a36e591 · outbound

This paper cites Accelerating power flow calcu- lations in lv networks using physics-informed graph neural networks.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Accelerating power flow calcu- lations in lv networks using physics-informed graph neural networks

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Observation 0d1a3d2d-7ab7-45cc-b966-b8470321693c · outbound

This paper cites Physical activation functions (pafs): An approach for more efficient induction of physics into physics-informed neural networks (pinns).

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Physical activation functions (pafs): An approach for more efficient induction of physics into physics-informed neural networks (pinns)

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.395948Z

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Observation a798f3ac-c260-4cc7-9386-96ec12f64369 · outbound

This paper cites Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications

Reference 43

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Observation 0dfde28b-beb9-405f-9eb7-9ad4c448a3df · outbound

This paper cites Scientific machine learning through physics–informed neural networks: Where we are and what’s next.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Scientific machine learning through physics–informed neural networks: Where we are and what’s next

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.384718Z

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Observation 419d546f-6c67-4856-bde8-4ecfcaa05b44 · outbound

This paper cites Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

Reference 45

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Observation ec88485a-663c-44af-b7fc-3b9b36fd241c · outbound

This paper cites Physics-informed deep neural networks for learning parameters and consti- tutive relationships in subsurface flow problems.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Physics-informed deep neural networks for learning parameters and consti- tutive relationships in subsurface flow problems

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.365203Z

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Observation 9b65ab4d-b1f7-4a01-966e-4eabca5273bd · outbound

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Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Unresolved cited work

Reference 47

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Observation 62c5cef7-d566-4d73-bc58-95782f5fa91f · outbound

This paper cites Understand- ing and mitigating gradient flow pathologies in physics- informed neural networks.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Understand- ing and mitigating gradient flow pathologies in physics- informed neural networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.334861Z

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Observation b9067ee8-78db-4e49-a785-9e3d13508c17 · outbound

This paper cites L., and Sbalzarini, I.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review L., and Sbalzarini, I

Reference 49

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Observation 82c0c2ca-54ef-42ee-90e0-5838a5cdad8c · outbound

This paper cites Self- adaptive physics-informed neural networks.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Self- adaptive physics-informed neural networks

Reference 50

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Observation 506e3bd2-ee9d-47b0-b1d7-a21c0c0df6c3 · outbound

This paper cites A dual-dimer method for training physics-constrained neural networks with minimax architecture.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review A dual-dimer method for training physics-constrained neural networks with minimax architecture

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.298104Z

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Observation 9b79f49d-66f9-4dc9-9789-9d914d203f28 · outbound

This paper cites The distribution of points in a cube and the approximate evaluation of integrals.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review The distribution of points in a cube and the approximate evaluation of integrals

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.288344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation af08178e-aa5c-4bf5-b667-ae84d72f421b · outbound

This paper cites On the efficiency of certain quasi-random sequences of points in evaluating multi- dimensional integrals.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review On the efficiency of certain quasi-random sequences of points in evaluating multi- dimensional integrals

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.277158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9b8b0e72-2ae7-4ceb-9b1d-ff19bd6a32af · outbound

This paper cites Monte carlo methods for solv- ing multivariable problems.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Monte carlo methods for solv- ing multivariable problems

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.266275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9bdcaa2e-4777-4c3e-8b95-e456c0bdcf58 · outbound

This paper cites A comparison of three methods for selecting values of input variables in the analysis of output from a computer code.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review A comparison of three methods for selecting values of input variables in the analysis of output from a computer code

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.254397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4e3758eb-e8df-410f-8818-74a984c8712f · outbound

This paper cites Not all sam- ples are created equal: Deep learning with importance sam- pling.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Not all sam- ples are created equal: Deep learning with importance sam- pling

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.239951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fcaf447d-cab9-4937-8243-2c61a9cb2b51 · outbound

This paper cites Variance Reduction in SGD by Distributed Importance Sampling.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Variance Reduction in SGD by Distributed Importance Sampling

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T17:24:49.009550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7864032a-e17c-4945-be59-fc77e74e6bdf · outbound

This paper cites Efficient training of physics-informed neural networks via importance sampling.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Efficient training of physics-informed neural networks via importance sampling

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.222750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation eed1af24-47b0-49a5-9f62-acc5d94e844f · outbound

This paper cites Biased Importance Sampling for Deep Neural Network Training.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Biased Importance Sampling for Deep Neural Network Training

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T17:24:49.016554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1a5b040-59ea-40ae-aacc-fc814100421a · outbound

This paper cites Das-pinns: A deep adaptive sampling method for solving high-dimensional partial differential equations.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Das-pinns: A deep adaptive sampling method for solving high-dimensional partial differential equations

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.212731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c728ec5b-6a21-46de-b0bf-87fa8e391197 · outbound

This paper cites Generative ad- versarial networks: An overview.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Generative ad- versarial networks: An overview

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.202619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7573598d-070f-4ea3-abe6-a8e224e055e0 · outbound

This paper cites When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:24:49.063277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7c120bcb-88e3-47b2-ba34-57f16cf1df45 · outbound

This paper cites Automatic control in microelectronics manufactur- ing: Practices, challenges, and possibilities.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Automatic control in microelectronics manufactur- ing: Practices, challenges, and possibilities

Reference 2000

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.817637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 49385967-42e8-40ba-b0a2-eb50048cf58e · outbound

This paper cites Microscopic modeling and optimal operation of plasma enhanced atomic layer deposition.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Microscopic modeling and optimal operation of plasma enhanced atomic layer deposition

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.500589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f8c56610-0115-45c3-8b91-46ba7e2cdc64 · outbound

This paper cites Physics-informed neural networks with hard con- straints for inverse design.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Physics-informed neural networks with hard con- straints for inverse design

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.345570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2a691b75-85d2-4da8-9d27-2869cfb5263b · outbound

This paper cites Inverse dirichlet weighting enables reliable train- ing of physics-informed neural networks.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Inverse dirichlet weighting enables reliable train- ing of physics-informed neural networks

Reference 2023

Resolution: verified exact; raw_fallback, observed 2026-08-06T17:24:49.179692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 648a4136-a8c4-4073-8bd0-ee81614680fb · outbound

This paper cites Opinion mining by convolutional neural networks for maximizing discoverability of nanomaterials.

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review Opinion mining by convolutional neural networks for maximizing discoverability of nanomaterials

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:24:49.539602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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