DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining | Paper Citation Record

Observation 5fdda2d6-3392-475c-b35f-e10378b9809f · outbound

This paper cites Language models are few-shot learners.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Language models are few-shot learners

Reference 1

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Observation ccc74661-305a-4826-9775-9cd511f5f7ed · outbound

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

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 2

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Observation a9d45260-dae4-447d-a5f0-d0a2c454248d · outbound

This paper cites Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks

Reference 3

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Observation c4b09faa-42ad-4d90-b42e-17a3fa85206f · outbound

This paper cites Learning transferable visual models from natural language supervision.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Learning transferable visual models from natural language supervision

Reference 4

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This paper cites Large-scale chemical language representations capture molecular structure and properties.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Large-scale chemical language representations capture molecular structure and properties

Reference 5

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This paper cites Accurate prediction of molecular properties and drug targets using a self- supervised image representation learning framework.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Accurate prediction of molecular properties and drug targets using a self- supervised image representation learning framework

Reference 6

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Observation e0405e74-377c-4a5a-a424-e333ecc5cc54 · outbound

This paper cites Deep Graph Infomax.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Deep Graph Infomax

Reference 7

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Observation 7decce05-1842-4ebe-9d79-104309348cb2 · outbound

This paper cites InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

Reference 8

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Observation 8c63a00f-55f3-4ebd-80b9-eab654a074ea · outbound

This paper cites Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare

Reference 9

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Observation 34be89b2-c7ea-4bfb-8446-65471e3e2a2d · outbound

This paper cites Graph representation learning in biomedicine and healthcare.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Graph representation learning in biomedicine and healthcare

Reference 10

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Observation b3618c72-e08d-427e-9cf7-6f12ed92b2f2 · outbound

This paper cites Clinical feature vector generation using unsupervised graph representation learning from heterogeneous medical records.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Clinical feature vector generation using unsupervised graph representation learning from heterogeneous medical records

Reference 11

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Observation ba5680da-2cff-4e14-b1ab-e1cf1436c008 · outbound

This paper cites N-gram graph: Simple unsupervised representation for graphs, with applications to molecules.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining N-gram graph: Simple unsupervised representation for graphs, with applications to molecules

Reference 12

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Observation c126039b-21b0-4023-b779-f41e193f1dcb · outbound

This paper cites Molecular contrastive learning of representations via graph neural networks.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Molecular contrastive learning of representations via graph neural networks

Reference 13

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Observation 24929c46-6154-4b69-8aa3-636a6d4efecb · outbound

This paper cites Molecular graph representation learning integrating large language models with domain-specific small models.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Molecular graph representation learning integrating large language models with domain-specific small models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.757677Z

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Observation 4590d492-db10-4cd0-9ab6-b37aefc35220 · outbound

This paper cites The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Reference 15

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Observation 53a4c83f-aa8b-4d1a-a017-d546b293dc3e · outbound

This paper cites Functionality matters in netlist representation learning.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Functionality matters in netlist representation learning

Reference 16

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Observation 7ad473b1-2062-401d-8df5-990f27b8afa5 · outbound

This paper cites Fgnn2: A powerful pre-training framework for learning the logic functionality of circuits.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Fgnn2: A powerful pre-training framework for learning the logic functionality of circuits

Reference 17

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Observation 1b327eb5-e716-4d55-ac61-6b0042090dbc · outbound

This paper cites Deepgate2: Functionality-aware circuit repre- sentation learning.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Deepgate2: Functionality-aware circuit repre- sentation learning

Reference 18

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Observation 6430ca3e-eb66-4696-9795-991a096aaff6 · outbound

This paper cites Deep- gate3: Towards scalable circuit representation learning.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Deep- gate3: Towards scalable circuit representation learning

Reference 19

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Observation e282a060-3555-43dd-b6a8-56e261e3b454 · outbound

This paper cites DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale

Reference 20

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Observation 0b7c7448-bcb5-42a9-82fb-3e800c23f786 · outbound

This paper cites An Empirical Study of Graph Contrastive Learning.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining An Empirical Study of Graph Contrastive Learning

Reference 21

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Observation d422ab98-6185-41f0-b0c3-e568ce694493 · outbound

This paper cites Towards Graph Contrastive Learning: A Survey and Beyond.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Towards Graph Contrastive Learning: A Survey and Beyond

Reference 22

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Observation 79dffdf0-664b-4a5d-942c-9169ca02236b · outbound

This paper cites Graph contrastive learning with augmentations.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Graph contrastive learning with augmentations

Reference 23

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Observation 564826e7-7c0f-43dc-8a32-8cb622136126 · outbound

This paper cites Infogcl: Information-aware graph contrastive learning.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Infogcl: Information-aware graph contrastive learning

Reference 24

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Observation 73a91ec9-bab8-44d2-858e-2e8f7c3bd996 · outbound

This paper cites Graph contrastive learning with adaptive augmentation.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Graph contrastive learning with adaptive augmentation

Reference 25

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Observation 5a321848-9b34-429a-ad28-3e1de2018aff · outbound

This paper cites Yosys-a free verilog synthesis suite.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Yosys-a free verilog synthesis suite

Reference 26

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Observation 3989bc4c-00e7-41ce-95a3-c12161dc3890 · outbound

This paper cites Abc: An academic industrial-strength verification tool.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Abc: An academic industrial-strength verification tool

Reference 27

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Observation aa07043c-0b00-4739-adf2-e04d7c2937e4 · outbound

This paper cites Deepgate: Learning neural representations of logic gates.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Deepgate: Learning neural representations of logic gates

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.538687Z

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Observation 0356ff7c-9bf9-43de-92f0-a6a58100a1cc · outbound

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

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Gcn-rl circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learning

Reference 29

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Observation cbb7040e-7a9e-46a2-9e94-6c0ebff22812 · outbound

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

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Autockt: Deep reinforcement learning of analog circuit designs

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.501902Z

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Observation 5ceef5fd-d21c-4c72-a1ea-6cd0bf38f25e · outbound

This paper cites Dnn-opt: An rl inspired optimization for analog circuit sizing using deep neural networks.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Dnn-opt: An rl inspired optimization for analog circuit sizing using deep neural networks

Reference 31

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Observation febf4c8f-fb07-4a98-8a36-0c535e1be032 · outbound

This paper cites INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers

Reference 32

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Observation 1b00d479-e5a1-4663-bd23-a782a093fb0f · outbound

This paper cites Learn-by-compare: Analog performance prediction using contrastive regression with design knowledge.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Learn-by-compare: Analog performance prediction using contrastive regression with design knowledge

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.461719Z

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Observation 1307e509-085e-4867-b065-9156ecb3469f · outbound

This paper cites A simple framework for contrastive learning of visual representations.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining A simple framework for contrastive learning of visual representations

Reference 34

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Observation 712ce2eb-19a5-4df8-b5a7-6992918531bc · outbound

This paper cites Exploring simple siamese representation learning.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Exploring simple siamese representation learning

Reference 35

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Observation 2ebf639d-b16c-4a7e-80af-ccdf9743f40e · outbound

This paper cites Dag-aware aig rewriting a fresh look at combinational logic synthesis.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Dag-aware aig rewriting a fresh look at combinational logic synthesis

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.423927Z

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Observation fa99ecfa-4c62-4d25-b130-a253d6fb6f22 · outbound

This paper cites Data is all you need: Finetuning llms for chip design via an automated design-data augmentation framework.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Data is all you need: Finetuning llms for chip design via an automated design-data augmentation framework

Reference 37

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Observation 93ffe9a9-08c8-4461-8de4-7f9af4b7f153 · outbound

This paper cites Verilogeval: Evaluating large language models for verilog code generation.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Verilogeval: Evaluating large language models for verilog code generation

Reference 38

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Observation 4389207c-f31a-44eb-b0b2-d7f2c5df005d · outbound

This paper cites CircuitFusion: Multimodal Circuit Representation Learning for Agile Chip Design.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining CircuitFusion: Multimodal Circuit Representation Learning for Agile Chip Design

Reference 39

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Observation 74a66449-a7cc-4596-bca7-b27d076c38f8 · outbound

This paper cites Pretraining graph neural networks for few-shot analog circuit modeling and design.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Pretraining graph neural networks for few-shot analog circuit modeling and design

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.374186Z

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Observation 2fdd74d3-d76b-4a84-97f7-de8e234ee670 · outbound

This paper cites A robust automated analog circuits classification involving a graph neural network and a novel data augmentation strategy.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining A robust automated analog circuits classification involving a graph neural network and a novel data augmentation strategy

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.355990Z

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Observation 1b2e80b6-8a2b-4845-8611-2e50830fc960 · outbound

This paper cites A graph attention network based system for robust analog circuits’ structure recognition involving a novel data augmentation technique.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining A graph attention network based system for robust analog circuits’ structure recognition involving a novel data augmentation technique

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.337962Z

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Observation 62cf1f4f-bad5-4455-ae46-78bfb4aac613 · outbound

This paper cites AnalogCoder: Analog Circuit Design via Training-Free Code Generation.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining AnalogCoder: Analog Circuit Design via Training-Free Code Generation

Reference 43

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Observation 8633f39b-e489-4ac2-94fa-7475c2d569ea · outbound

This paper cites SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-07T23:14:10.728218Z

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Observation cb8cf2f8-12b3-4fdb-ba5d-19fb6c3a31db · outbound

This paper cites Masala-CHAI: A Large-Scale SPICE Netlist Dataset for Analog Circuits by Harnessing AI.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Masala-CHAI: A Large-Scale SPICE Netlist Dataset for Analog Circuits by Harnessing AI

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T23:14:10.355326Z

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Observation 3b976a8a-cdb7-4144-a95d-213c4c510691 · outbound

This paper cites Paragraph: Layout parasitics and device parameter prediction using graph neural networks.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Paragraph: Layout parasitics and device parameter prediction using graph neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.319520Z

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Observation 92fb7947-98c2-4aed-9ffa-e67cfe9e9e7b · outbound

This paper cites Pretraining graph neural networks for few-shot analog circuit modeling and design.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Pretraining graph neural networks for few-shot analog circuit modeling and design

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.302498Z

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Observation 51c2af2d-3453-4c7d-ad9d-5bf6345a4a1b · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Semi-Supervised Classification with Graph Convolutional Networks

Reference 48

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Observation 00ded8f7-0693-41b0-b24a-a630acdf1117 · outbound

This paper cites Inductive representation learning on large graphs.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Inductive representation learning on large graphs

Reference 49

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Observation 8f46c0d1-a9f5-4c53-82ca-7fad92850716 · outbound

This paper cites Graph attention networks.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Graph attention networks

Reference 50

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Observation 884e38ab-5c10-4e1f-820a-f45c7d6da7f0 · outbound

This paper cites How Powerful are Graph Neural Networks?.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining How Powerful are Graph Neural Networks?

Reference 51

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Observation b47dff0a-d05a-4b60-b9a8-f96eb5bf7c28 · outbound

This paper cites Noception: A fast ppa prediction framework for network-on-chips using graph neural network.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Noception: A fast ppa prediction framework for network-on-chips using graph neural network

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.258048Z

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Observation 33d25feb-3731-4409-ad1c-44414e5fc354 · outbound

This paper cites MasterRTL: A Pre-Synthesis PPA Estimation Framework for Any RTL Design.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining MasterRTL: A Pre-Synthesis PPA Estimation Framework for Any RTL Design

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-07T23:14:10.657849Z

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Observation f5584e70-8265-4d69-a205-5801b041ad62 · outbound

This paper cites PowPrediCT: Cross-stage power prediction with circuit-transformation-aware learning.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining PowPrediCT: Cross-stage power prediction with circuit-transformation-aware learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.238310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6d2a6073-b6cf-46d3-bc52-453f2e23e7ac · outbound

This paper cites Pan, and Yibo Lin.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Pan, and Yibo Lin

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.220751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3495e175-6ae9-47bd-b9a4-69697c093922 · outbound

This paper cites CircuitSeer: RTL post-pnr delay prediction via coupling functional and structural representation.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining CircuitSeer: RTL post-pnr delay prediction via coupling functional and structural representation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.205094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 99ff807a-ec00-4802-9675-fced952deffd · outbound

This paper cites LHNN: Lattice hypergraph neural network for vlsi congestion prediction.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining LHNN: Lattice hypergraph neural network for vlsi congestion prediction

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.187313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2d7ec4ce-6057-4512-bb2c-8b1bf171586a · outbound

This paper cites PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T23:14:10.421172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73ea0f6c-9c43-4104-9d88-e912c203a469 · outbound

This paper cites GNN-RE: Graph neural networks for reverse engineering of gate-level netlists.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining GNN-RE: Graph neural networks for reverse engineering of gate-level netlists

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.169032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bb7f8e40-b96d-48b1-9d16-13ac004a3063 · outbound

This paper cites Appgnn: Approximation-aware functional reverse engineering using graph neural networks.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Appgnn: Approximation-aware functional reverse engineering using graph neural networks

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.150819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1dbcc881-ad78-4dad-ba6c-6f6c6e0eca3e · outbound

This paper cites Graph of circuits with gnn for exploring the optimal design space.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Graph of circuits with gnn for exploring the optimal design space

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.133822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation df8e7e3b-fdde-4943-8acf-c46b5c264807 · outbound

This paper cites Circuit-gnn: A graph neural network for transistor-level modeling of analog circuit hierarchies.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Circuit-gnn: A graph neural network for transistor-level modeling of analog circuit hierarchies

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.115545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1ec7a1e6-50a1-4b82-9f11-f2becf0a50bd · outbound

This paper cites Khamis and Mohammed Agamy.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Khamis and Mohammed Agamy

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.097939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3fcbe442-eaec-4d17-9035-acf057daa46a · outbound

This paper cites Turner, George F.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Turner, George F

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.078712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2608c597-a13c-456b-983d-23fd3e5e6dda · outbound

This paper cites an unresolved cited work.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-07T23:14:11.062750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3585e6b6-445e-403c-833d-e46b97fa0a41 · outbound

This paper cites Pulserf: Physics augmented ml mod- eling and synthesis for high-frequency rfic design.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Pulserf: Physics augmented ml mod- eling and synthesis for high-frequency rfic design

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.045569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8db7b37f-3f6f-4c68-9fbf-febf258a6c83 · outbound

This paper cites Gnn-based hierarchi- cal annotation for analog circuits.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Gnn-based hierarchi- cal annotation for analog circuits

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.028770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 02ec12f3-c396-4dd4-ad46-52373f778ead · outbound

This paper cites Graph attention-based symmetry constraint extraction for analog circuits.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Graph attention-based symmetry constraint extraction for analog circuits

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:11.010982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ed9fbea2-daac-494e-855f-3b092d9062c5 · outbound

This paper cites CktGNN: Circuit Graph Neural Network for Electronic Design Automation.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining CktGNN: Circuit Graph Neural Network for Electronic Design Automation

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T23:14:10.475858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42bf6fc9-c006-453c-a745-ef26eb3eadfb · outbound

This paper cites Analoggym: An open and practical testing suite for analog circuit synthesis.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Analoggym: An open and practical testing suite for analog circuit synthesis

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:10.987613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a4c2df60-ce6f-458e-8224-78bf0884c093 · outbound

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

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-07T23:14:10.601142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation daa60b6b-d810-4b02-820e-3ec71e72cc78 · outbound

This paper cites DeeperGCN: All You Need to Train Deeper GCNs.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining DeeperGCN: All You Need to Train Deeper GCNs

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T23:14:10.491268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c957110-9c39-488b-b5d0-323f597cb5f5 · outbound

This paper cites AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T23:14:10.496934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bba5654d-3e6b-496a-af74-ba08319b5fc9 · outbound

This paper cites Baker, Yuan-En Sun, Qi Tang, and Bao Wang.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Baker, Yuan-En Sun, Qi Tang, and Bao Wang

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:10.970583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8f6ad5aa-e844-40c1-8200-79e0abed0bd5 · outbound

This paper cites Domain knowledge-infused deep learning for automated analog/radio-frequency circuit parameter optimization.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Domain knowledge-infused deep learning for automated analog/radio-frequency circuit parameter optimization

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:10.953379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e0425ae-d690-4978-a58a-871ab1918989 · outbound

This paper cites Rose: Robust analog circuit parameter optimization with sampling-efficient reinforcement learning.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Rose: Robust analog circuit parameter optimization with sampling-efficient reinforcement learning

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:10.936528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b7efb39a-63c6-424a-952b-b6ba347e6161 · outbound

This paper cites Rose-opt: Robust and efficient analog circuit parameter optimization with knowledge-infused reinforce- ment learning.

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining Rose-opt: Robust and efficient analog circuit parameter optimization with knowledge-infused reinforce- ment learning

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:10.918726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers