Paper Citation Record · LEDGER

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction

As of 6 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2606.11247.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.11247 v1

Coverage vector

measured 43 of 43 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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A source-named dated measurement, never combined with another source.

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Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation d7c34300-0989-4036-aaef-64f4f5e63e82 · outbound

This paper cites Deep learning for smart manufacturing: Methods and applications,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Deep learning for smart manufacturing: Methods and applications,

Reference 1

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This paper cites an unresolved cited work.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Unresolved cited work

Reference 2

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Observation 1ab19b57-5b28-4f9a-902a-d97f483f4c9e · outbound

This paper cites Big data analytics for smart manufacturing: Case studies in semiconductor manufacturing,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Big data analytics for smart manufacturing: Case studies in semiconductor manufacturing,

Reference 3

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This paper cites Automated quality and process control for additive manufacturing using deep convolutional neural networks,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Automated quality and process control for additive manufacturing using deep convolutional neural networks,

Reference 4

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Observation 50fc8362-ce7b-4246-b977-c39327934ba8 · outbound

This paper cites Smart additive manufacturing empowered by a closed -loop machine learning algorithm,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Smart additive manufacturing empowered by a closed -loop machine learning algorithm,

Reference 5

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This paper cites On the use of machine learning for additive manufacturing technology in Industry 4.0,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction On the use of machine learning for additive manufacturing technology in Industry 4.0,

Reference 6

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Observation ac205069-7b59-45d2-890c-07000f12d778 · outbound

This paper cites Toward digital twins in 3D IC packaging: A critical review of physics, data, and hybrid architectures,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Toward digital twins in 3D IC packaging: A critical review of physics, data, and hybrid architectures,

Reference 7

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Observation f2bedae6-5025-4df9-8d69-0c9c45700745 · outbound

This paper cites Moyne, E.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Moyne, E

Reference 8

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This paper cites Thermomechanical challenges of 2.5 -D packaging: A review of warpage and interconnect reliability,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Thermomechanical challenges of 2.5 -D packaging: A review of warpage and interconnect reliability,

Reference 9

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Observation 272ffbf7-c988-495d-9e94-ab6059338d20 · outbound

This paper cites Generative adversarial nets,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Generative adversarial nets,

Reference 10

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Observation b6654199-7c40-40e0-bf90-2724f0d628f2 · outbound

This paper cites Denoising diffusion probabilistic models,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Denoising diffusion probabilistic models,

Reference 11

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Observation c7fb7f42-09aa-4a40-b527-4c6257097a38 · outbound

This paper cites How Can Large Language Models Help Humans in Design and Manufacturing?.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction How Can Large Language Models Help Humans in Design and Manufacturing?

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:57:28.985396Z

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Observation 281cbfe7-2ada-4577-9ab1-aa0f70ff936d · outbound

This paper cites Deep generative models in engineering design: A review,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Deep generative models in engineering design: A review,

Reference 13

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This paper cites Diffusion models beat GANs on topology optimization,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Diffusion models beat GANs on topology optimization,

Reference 14

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This paper cites GAN -OPC: Mask optimization with lithography -guided generative adversarial nets,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction GAN -OPC: Mask optimization with lithography -guided generative adversarial nets,

Reference 15

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This paper cites Wafer map defect pattern classification and image retrieval using convolutional neural network,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Wafer map defect pattern classification and image retrieval using convolutional neural network,

Reference 16

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This paper cites Deep learning for the design of photonic structures,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Deep learning for the design of photonic structures,

Reference 17

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Observation e2a6d4ed-fcc8-424b-aab4-43ff73900bce · outbound

This paper cites Improvement of TCAD augmented machine learning using autoencoder for semiconductor variation identification and inverse design,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Improvement of TCAD augmented machine learning using autoencoder for semiconductor variation identification and inverse design,

Reference 18

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Observation 438fa768-86ef-493f-9415-3ce0dabd8107 · outbound

This paper cites Physics -informed machine learning,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Physics -informed machine learning,

Reference 19

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Observation 7d28b5a5-84ad-44ec-8159-6f361b305a7f · outbound

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

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 20

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This paper cites A physics -informed diffusion model for high -fidelity flow field reconstruction,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction A physics -informed diffusion model for high -fidelity flow field reconstruction,

Reference 21

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction MxDiffusion: A physics-aware Maxwell’s law-guided diffusion model strategy for inverse photonic metasurface design,

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This paper cites Inverse lithography physics -informed deep neural level set for mask optimization,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Inverse lithography physics -informed deep neural level set for mask optimization,

Reference 23

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction DiffTaichi: Differentiable programming for physical simulation,

Reference 24

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Physical modeling of graphene nanoribbon field effect transistor using non -equilibrium Green function approach for integrated circuit design,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction A novel graphene nanoribbon field effect transistor for integrated circuit design,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Fourier neural operator for parametric partial differential equations,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Hamiltonian neural networks,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction ChipNeMo: Domain-Adapted LLMs for Chip Design

Reference 30

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Observation 25fdc8cf-4955-4d00-a2e8-aed8ed2fa1cf · outbound

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Inverse design in nanophotonics,

Reference 31

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Physics-guided and fabrication-aware inverse design of photonic devices using diffusion models,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction EDMNet: Unveiling the power of machine learning in regression modeling of powder mixed-EDM,

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This paper cites LithoBench: Benchmarking AI computational lithography for semiconductor manufacturing,.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction LithoBench: Benchmarking AI computational lithography for semiconductor manufacturing,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Closed -loop optimization of fast -charging protocols for batteries with machine learning,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction An autonomous laboratory for the accelerated synthesis of novel materials,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction On -the-fly closed-loop materials discovery via Bayesian active learning,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Autonomous experimentation systems for materials development: A community perspective,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction A Tutorial on Bayesian Optimization

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Taking the human out of the loop: A review of Bayesian optimization,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Unresolved cited work

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Characterising the digital twin: A systematic literature review,

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction Score-based generative modeling through stochastic differential equations,

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