Paper Citation Record · LEDGER

Analysis of Photonic Circuit Losses with Machine Learning Techniques

As of 17 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2506.17999.

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

pith.paper-citation-record.v1
2506.17999 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:27:17.833791Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact0
verified fuzzy43
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 00cd7bcb-f3fe-4bae-b56f-6d6023fe8cc6 · outbound

This paper cites Review of silicon photonics technology and platform development,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Review of silicon photonics technology and platform development,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.951709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9f4c29c9-e2ae-4b91-ba58-21626e4f1a29 · outbound

This paper cites Wafer-scale demonstration of low-loss (∼0.43 dB/cm), high-bandwidth (>38 GHz), silicon photonics platform operating at the C-band,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Wafer-scale demonstration of low-loss (∼0.43 dB/cm), high-bandwidth (>38 GHz), silicon photonics platform operating at the C-band,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.943620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8e201ce8-fd0f-472e-961a-2e4cd0e7e0ec · outbound

This paper cites High-yield, wafer-scale fabrication of ultralow-loss, dispersion-engineered silicon nitride photonic circuits,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques High-yield, wafer-scale fabrication of ultralow-loss, dispersion-engineered silicon nitride photonic circuits,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.934505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 489c884e-91ef-4a4a-882d-784599719454 · outbound

This paper cites Foundry manufacturing of tight-confinement, dispersion-engineered, ultralow-loss silicon nitride photonic integrated circuits,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Foundry manufacturing of tight-confinement, dispersion-engineered, ultralow-loss silicon nitride photonic integrated circuits,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.926712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 53df6b26-6048-44e9-96bb-61e11f1e7e7e · outbound

This paper cites Silicon quantum photonics,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Silicon quantum photonics,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.917924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0b061c1c-33a1-4385-8429-b9ac526fabd8 · outbound

This paper cites Silicon nitride passive and active photonic integrated circuits: trends and prospects,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Silicon nitride passive and active photonic integrated circuits: trends and prospects,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.910123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7110d777-471b-46a9-9352-b3abd179e150 · outbound

This paper cites Capturing the effects of spatial process variations in silicon photonic circuits,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Capturing the effects of spatial process variations in silicon photonic circuits,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.902457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f116e6bd-06ed-4c4a-a2c1-c5d19e02c859 · outbound

This paper cites Process variation in silicon photonic devices,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Process variation in silicon photonic devices,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.893909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dab6007a-2770-4f70-9fce-3fb9fb9ba54b · outbound

This paper cites Optical quantum computing,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Optical quantum computing,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.885509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1e8a1f55-faf6-4413-85f5-69f58201c551 · outbound

This paper cites Nanoscale low crosstalk photonic crystal integrated sensor array,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Nanoscale low crosstalk photonic crystal integrated sensor array,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.877304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5cee7b17-a2b9-4779-b608-4cd84c0981ea · outbound

This paper cites Performancepredictionforsiliconphotonicsintegratedcircuitswithlayout-dependent correlated manufacturing variability,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Performancepredictionforsiliconphotonicsintegratedcircuitswithlayout-dependent correlated manufacturing variability,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.869958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 170161fe-ec0c-40fc-b320-a6230eb807f1 · outbound

This paper cites Modelling fabrication variability in silicon photonic devices.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Modelling fabrication variability in silicon photonic devices

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.862864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e63d48ea-fcd3-4358-b790-1a873a9c3616 · outbound

This paper cites Advances in machine learning for large-scale manufacturing of photonic circuits,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Advances in machine learning for large-scale manufacturing of photonic circuits,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.854689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 44dc4941-ccae-4473-a9dc-a54168ada155 · outbound

This paper cites Machine learning for mask/wafer hotspot detection and mask synthesis,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Machine learning for mask/wafer hotspot detection and mask synthesis,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.847320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 08649b9b-2249-49da-9a48-6e0d2eef3b95 · outbound

This paper cites Use of neural network to model the deposition rate of PECVD-silicon nitride films,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Use of neural network to model the deposition rate of PECVD-silicon nitride films,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.838845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 83ad0fc0-3940-4a0e-8205-ac18a8e27f9e · outbound

This paper cites Regression methods for prediction of PECVD silicon nitride layer thickness,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Regression methods for prediction of PECVD silicon nitride layer thickness,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.831130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d09571bb-e12e-4589-b997-b73c65137db1 · outbound

This paper cites Neural network modeling of inter-characteristics of silicon nitride film deposited by using a plasma-enhanced chemical vapor deposition,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Neural network modeling of inter-characteristics of silicon nitride film deposited by using a plasma-enhanced chemical vapor deposition,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.821667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 41e07cca-b31b-469f-a09b-db362fe10c3a · outbound

This paper cites Evaluating regression-based techniques for modelling fabrication variations in silicon photonic waveguides,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Evaluating regression-based techniques for modelling fabrication variations in silicon photonic waveguides,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.812681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6bdae7ef-7272-4d89-a9ca-667c860dc905 · outbound

This paper cites Random forest modelling as a tool for propagation and bend excess loss minimization on silicon nitride waveguide platforms,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Random forest modelling as a tool for propagation and bend excess loss minimization on silicon nitride waveguide platforms,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.803967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5980db0e-2c14-4ba2-bb14-af7ab43e74a2 · outbound

This paper cites Deep learning models for effective refractive indices in silicon nitride waveguides,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Deep learning models for effective refractive indices in silicon nitride waveguides,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.795164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 687c16d7-ca41-4686-9b26-fa98b28df7d2 · outbound

This paper cites Improved waveguide surface roughness by foundry-processing techniques for enhanced light delivery to integrated ion trap for quantum computing platforms,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Improved waveguide surface roughness by foundry-processing techniques for enhanced light delivery to integrated ion trap for quantum computing platforms,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.785833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b3abea37-ef1f-474c-85a4-a0dcaaf7f393 · outbound

This paper cites A study of cross-validation and bootstrap for accuracy estimation and model selection,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques A study of cross-validation and bootstrap for accuracy estimation and model selection,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.777114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 20d7c350-41d5-4be3-aed8-84c75deff6b3 · outbound

This paper cites Linearregression,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Linearregression,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.768984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 55b0518d-61fa-4a87-86b8-ccce24c51ddd · outbound

This paper cites A survey of feature selection and feature extraction techniques in machine learning,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques A survey of feature selection and feature extraction techniques in machine learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.760280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c4fbb306-43f0-4f84-a4ad-99e55917c902 · outbound

This paper cites Regression modeling strategies,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Regression modeling strategies,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.752373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c546124b-a837-4fed-a68e-0fca950bac0d · outbound

This paper cites Generalized linear models,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Generalized linear models,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.744877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fb2c8b7d-4bd5-41f0-8621-64b72bb1479b · outbound

This paper cites Regression shrinkage and selection via the lasso,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Regression shrinkage and selection via the lasso,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T23:27:16.406783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db4df20e-14e6-45c5-910b-5b9ec26e32cf · outbound

This paper cites Induction of decision trees,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Induction of decision trees,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.732867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f8b0b802-cf9f-485d-a724-6e88808e1e57 · outbound

This paper cites Random forests,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Random forests,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.725527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 956c3a3b-f620-456a-894a-e0c349f9439c · outbound

This paper cites Scikit-learn: Machine learning in python,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Scikit-learn: Machine learning in python,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.717340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b9111249-99e7-4278-bf3f-9ce58816549a · outbound

This paper cites A tutorial on support vector regression,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques A tutorial on support vector regression,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.709763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ceb94932-917b-48ba-b501-46bfb3a69e02 · outbound

This paper cites Kernel ridge regression,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Kernel ridge regression,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.702285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f7f2aedc-92c8-40b8-8e45-5e2395d2bb93 · outbound

This paper cites TensorFlow: Large-scale machine learning on heterogeneous systems,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques TensorFlow: Large-scale machine learning on heterogeneous systems,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.695326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 16022adb-7028-4d30-a34f-38bdeb9be051 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Adam: A Method for Stochastic Optimization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T23:27:17.176030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d576032-2019-4ba6-9260-8776cb043cf9 · outbound

This paper cites No unbiased estimator of the variance of k-fold cross-validation,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques No unbiased estimator of the variance of k-fold cross-validation,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.688334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 10f9f256-8cf1-418c-ac28-e18d65163ad3 · outbound

This paper cites On estimating model accuracy with repeated cross-validation,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques On estimating model accuracy with repeated cross-validation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.681314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0ad64025-749c-45ef-bcd6-8f6ccd2a7b8c · outbound

This paper cites Cross-validation: what does it estimate and how well does it do it?.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Cross-validation: what does it estimate and how well does it do it?

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.675039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 95d97fd2-0c3c-4fc4-a8e6-4e8eb6afa519 · outbound

This paper cites Plasma enhanced chemical vapor deposition silicon oxynitride optimized for application in integrated optics,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Plasma enhanced chemical vapor deposition silicon oxynitride optimized for application in integrated optics,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.668097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c724927f-07c2-4b99-b5eb-d81eceae6bd4 · outbound

This paper cites Comparative investigation of hydrogen bonding in silicon based PECVD grown dielectrics for optical waveguides,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Comparative investigation of hydrogen bonding in silicon based PECVD grown dielectrics for optical waveguides,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.661669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f631a548-781d-4801-9736-ee84662a3329 · outbound

This paper cites Low loss Si3N4-SiO2 optical waveguides on Si,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Low loss Si3N4-SiO2 optical waveguides on Si,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.654469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8b9c4250-7cae-4bba-a0c4-11ddaafab692 · outbound

This paper cites Nonlinear silicon nitride waveguides based on a PECVD deposition platform,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Nonlinear silicon nitride waveguides based on a PECVD deposition platform,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.647319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0a859d1b-dc4e-4a58-83b8-81b3665a64a3 · outbound

This paper cites CMOS compatible monolithic multi-layer Si3N4-on-SOI platform for low-loss high performance silicon photonics dense integration,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques CMOS compatible monolithic multi-layer Si3N4-on-SOI platform for low-loss high performance silicon photonics dense integration,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.640163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e5452e61-478f-4a96-87fd-f557373dfafe · outbound

This paper cites Material and optical properties of low-temperature NH3-free PECVD SiNx layers for photonic applications,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Material and optical properties of low-temperature NH3-free PECVD SiNx layers for photonic applications,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.495427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b7bbdd3d-d414-421e-b92a-fe3b34e1d005 · outbound

This paper cites Asurveyofdeepneuralnetworkarchitecturesandtheirapplications,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques Asurveyofdeepneuralnetworkarchitecturesandtheirapplications,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:18.258611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 67d9afca-7088-4e53-8bc4-dab220f9c8e7 · outbound

This paper cites The Beer-Lambert law,.

Analysis of Photonic Circuit Losses with Machine Learning Techniques The Beer-Lambert law,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:17.967016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.