Paper Citation Record · LEDGER

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights

As of 21 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2501.11839.

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

pith.paper-citation-record.v1
2501.11839 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:52:27.698346Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:21:43.250875Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T20:21:43.737358Z

Reference resolution

34 of 34 outbound references displayed

verified exact0
verified fuzzy28
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 86621012-ae20-4c67-b210-cfafd5c2ded4 · outbound

This paper cites The role of millimeter- wave technologies in 5g/6g wireless communications,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights The role of millimeter- wave technologies in 5g/6g wireless communications,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:28.170409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0c32b60e-64dc-4698-b997-d1db5d034f77 · outbound

This paper cites A mmwave automotive joint radar-communications system,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights A mmwave automotive joint radar-communications system,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:28.156724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c70ca6a5-55be-4ad3-8823-96b484badc7a · outbound

This paper cites Design of a high-power gaussian pulse transmitter for sensing and imaging of buried objects,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Design of a high-power gaussian pulse transmitter for sensing and imaging of buried objects,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:28.143342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4101e14e-0f9a-4519-8ce4-cf9010fea128 · outbound

This paper cites A 55nm saw- less nb-iot cmos transceiver in an rf-soc with phase coherent rx and polar modulation tx,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights A 55nm saw- less nb-iot cmos transceiver in an rf-soc with phase coherent rx and polar modulation tx,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:28.130192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a7c6ab77-3f5a-4357-bff7-cf5f9cdafa80 · outbound

This paper cites Cryogenic cmos rf circuits: A promising approach for large-scale quantum computing,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Cryogenic cmos rf circuits: A promising approach for large-scale quantum computing,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:28.116985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation acfbd368-0c4e-4930-8eb2-78e8589856f6 · outbound

This paper cites A cmos high-power broadband 260-ghz radiator array for spectroscopy,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights A cmos high-power broadband 260-ghz radiator array for spectroscopy,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:28.103179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3e02d029-313e-46a0-886b-ddad06771534 · outbound

This paper cites Moore’s law: past, present and future,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Moore’s law: past, present and future,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:28.087834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a51ea22f-ca15-43d1-a1d4-e39f3531dfce · outbound

This paper cites Automation framework for digital circuit design and verification,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Automation framework for digital circuit design and verification,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:28.073716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7d94c0eb-a941-4d66-96d3-f9f702883bf7 · outbound

This paper cites Towards the automatic design of more efficient digital circuits,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Towards the automatic design of more efficient digital circuits,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:28.059732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 88173291-e29e-4622-a8e6-e42cb3337327 · outbound

This paper cites an unresolved cited work.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:52:28.045951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f79a2d1f-4287-4d14-9406-55eb77d24f3c · outbound

This paper cites Computer-aided design of analog and mixed-signal integrated circuits,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Computer-aided design of analog and mixed-signal integrated circuits,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:28.032449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 87bb5d3b-3661-4962-a30e-8b1d9e0f8384 · outbound

This paper cites an unresolved cited work.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Unresolved cited work

Reference 12

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Source-reported events for the cited work

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Observation 31014633-d5d2-4321-9169-d068e9ef8a3c · outbound

This paper cites Razavi, RF Microelectronics, 2nd ed.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Razavi, RF Microelectronics, 2nd ed

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:28.009816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6f4556df-bf97-4e5c-b060-0aeb1307a0d2 · outbound

This paper cites Bagnet: Berkeley analog generator with layout optimizer boosted with deep neural networks,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Bagnet: Berkeley analog generator with layout optimizer boosted with deep neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.996124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ff9dd721-38b7-4575-b037-d0864ace40c3 · outbound

This paper cites Autockt: deep reinforcement learning of analog circuit designs,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Autockt: deep reinforcement learning of analog circuit designs,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.981735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 958cfa75-7cea-496b-8df8-547605a17149 · outbound

This paper cites Angel: Fully- automated analog circuit generator using a neural network assisted semi-supervised learning approach,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Angel: Fully- automated analog circuit generator using a neural network assisted semi-supervised learning approach,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.967984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f76ddb7b-3dbb-4ff4-b4dc-93ee42352ac7 · outbound

This paper cites Gcn-rl circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learning,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Gcn-rl circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learning,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:27.624652Z

Source-reported events for the cited work

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Observation ea94cc74-3fae-4c1f-a34c-bed0ab6d37e0 · outbound

This paper cites Learning to design analog circuits to meet threshold specifications,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Learning to design analog circuits to meet threshold specifications,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.944700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4e5c4de3-296b-4cf1-a525-3ae4ee5e81f4 · outbound

This paper cites AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.931063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b49c9b6a-f38f-4add-bee1-bf05c55a0976 · outbound

This paper cites A bayesian optimization framework for analog circuits optimization,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights A bayesian optimization framework for analog circuits optimization,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.916756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 21a74e07-7917-487b-b988-cd489194d9cc · outbound

This paper cites An efficient batch-constrained bayesian optimization approach for analog circuit synthesis via multiobjective acquisition ensemble,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights An efficient batch-constrained bayesian optimization approach for analog circuit synthesis via multiobjective acquisition ensemble,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.903371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1e4f2e4a-726b-4c96-8c43-e1fad84843b5 · outbound

This paper cites Deep reinforcement learning for analog circuit sizing,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Deep reinforcement learning for analog circuit sizing,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.889638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e0f361f8-0a89-4d33-84d3-9b83e0f23fdd · outbound

This paper cites Razavi, Design of analog CMOS integrated circuits.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Razavi, Design of analog CMOS integrated circuits

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.875653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fc1aa75d-085d-42df-ab2b-ccfb855de4c1 · outbound

This paper cites Cmos wireless transceivers: the new wave,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Cmos wireless transceivers: the new wave,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.860962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a7f65c15-04f1-4133-bbfc-91be564287a9 · outbound

This paper cites A 49-63 ghz phase-locked fmcw radar transceiver for high resolution applications,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights A 49-63 ghz phase-locked fmcw radar transceiver for high resolution applications,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.847130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dd584047-8cc6-47de-925b-de616f2c11eb · outbound

This paper cites Li and M.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Li and M

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.832431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e16d2fc3-f03e-4e5b-b4b2-874516c0d598 · outbound

This paper cites Cadence design environment,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Cadence design environment,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.819054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 97b123af-bc5e-44c3-8bac-be017bcff96b · outbound

This paper cites Attention is all you need,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Attention is all you need,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.804806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1080f674-a4c7-42b8-a8d0-987828fbab8c · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:27.677507Z

Source-reported events for the cited work

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Observation 2cad4439-d053-481a-9c75-8b36c0191061 · outbound

This paper cites Random forests,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Random forests,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:27.681985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95cd37dc-2370-4e61-9f7d-3cce5040f2c7 · outbound

This paper cites K-nearest neighbors,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights K-nearest neighbors,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.773148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 28d93e06-613c-4d4f-af93-29f5f10f3e9e · outbound

This paper cites Support vector regres- sion,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Support vector regres- sion,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.758818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8663e04c-54f6-41f2-a4d7-e8af45f2140c · outbound

This paper cites 2 a primer on kernel methods,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights 2 a primer on kernel methods,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:52:27.743669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 240b2ff7-e1ab-4134-85ff-f15d2232839e · outbound

This paper cites Adam: A method for stochastic optimization,.

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights Adam: A method for stochastic optimization,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:27.698346Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 177ed7a6-a3cf-4ff7-952b-9073f04119bf · inbound

A Machine Learning Benchmarking Framework for Lipid Nanoparticle Transfection Efficiency Prediction cites this paper.

A Machine Learning Benchmarking Framework for Lipid Nanoparticle Transfection Efficiency Prediction Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:21:43.828273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.