[1]

Razavi B. 2000. Design of analog CMOS integrated circuits. New York: McGraw-Hill. 704 pp https://dl.acm.org/doi/book/10.5555/1594009

[2]

Oppenheim AV., Willsky AS, Nawab SH. 1997. Signals & systems. 2nd Edition. London: Pearson Education. 957 pp https://dl.acm.org/doi/book/10.5555/248702

[3]

Gray PR, Hurst P, Lewis SH, Meyer RG. 2009. Analysis and design of analog integrated circuits. 5th Edition. Hoboken: John Wiley & Sons. 896 pp

[4]

Tlelo-Cuautle E. 2013. Integrated circuits for analog signal processing. Berlin: Springer. 322 pp doi: 10.1007/978-1-4614-1383-7

[5]

Liu M, Ene TD, Kirby R, Cheng C, Pinckney N, et al. 2023. ChipNeMo: domain-adapted llms for chip design. arXiv Preprint: 2311.00176

doi: 10.48550/arXiv.2311.00176
[6]

Zhong R, Du X, Kai S, Tang Z, Xu S, et al. 2023. LLM4EDA: emerging progress in large language models for electronic design automation. arXiv Preprint: 2401.12224

doi: 10.48550/arXiv.2401.12224
[7]

Vladimirescu A. 1994. The SPICE book. Hoboken: John Wiley & Sons. 412 pp https://dl.acm.org/doi/10.5555/528264

[8]

Lai Y, Lee S, Chen G, Poddar S, Hu M, et al. 2025. Analogcoder: analog circuit design via training-free code generation. Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelligence, Philadelphia, 2025. Menlo Park: AAAI. pp. 379−387 doi: 10.1609/aaai.v39i1.32016

[9]

Shen J, Chen Z, Zhuang J, Huang J, Yang F, et al. 2026. Atelier: an automated analog circuit design framework via multiple large language model-based agents. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 45(1):31−44

doi: 10.1109/TCAD.2025.3573228
[10]

Huynh N, Lin B. 2025. Large language models for code generation: a comprehensive survey of challenges, techniques, evaluation, and applications. arXiv Preprint: 2503.01245

doi: 10.48550/arXiv.2503.01245
[11]

He Q, Zeng J, He Q, Liang J, Xiao Y. 2024. From complex to simple: enhancing multi-constraint complex instruction following ability of large language models. Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, 2024. Stroudsburg: ACL. pp. 10864−10882 doi: 10.18653/v1/2024.findings-emnlp.637

[12]

Wang H, Feng S, He T, Tan Z, Han X, et al. 2023. Can language models solve graph problems in natural language? Advances in Neural Information Processing Systems 36 (NeurIPS 2023), New Orleans, 2023. New York: Curran Associates. pp. 30840−30861 https://proceedings.neurips.cc/paper_files/paper/2023/file/622afc4edf2824a1b6aaf5afe153fa93-Paper-Conference.pdf

[13]

Zhang H, Sun S, Lin Y, Wang R, Bian J. 2025. AnalogXpert: automating analog topology synthesis by incorporating circuit design expertise into large language models. 2025 International Symposium of Electronics Design Automation (ISEDA), Hong Kong, 2025. Piscataway: IEEE. pp. 772−777 doi: 10.1109/ISEDA65950.2025.11100627

[14]

Shi Y, Tao Z, Gao Y, Zhou T, Chang C, et al. 2025. AMSnet-KG: a netlist dataset for LLM-based AMS circuit auto-design using knowledge graph RAG. ACM Transactions on Design Automation of Electronic Systems. https://doi.org/10.1145/3736166

[15]

Liu C, Olowe EA, Chitnis D. 2025. LLM-based AI agent for sizing of analog and mixed signal circuit. 2025 23rd IEEE Interregional NEWCAS Conference (NEWCAS), Paris, France, 2025. Piscataway, USA: IEEE. pp. 90−94 doi: 10.1109/NewCAS64648.2025.11107079

[16]

Rashid R, Krishna K, George CP, Nambath N. 2024. Machine learning driven global optimisation framework for analog circuit design. Microelectronics Journal 151:106362

doi: 10.1016/j.mejo.2024.106362
[17]

Hammoud A, Goyal C, Pathen S, Dai A, Li A, et al. 2024. Human language to analog layout using Glayout layout automation framework. Proceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD, Snowbird, 2024. New York: ACM. pp. 1−7 doi: 10.1145/3670474.3685971

[18]

Dong Z, Cao W, Zhang M, Tao D, Chen Y, et al. 2023. Cktgnn: circuit graph neural network for electronic design automation. arXiv Preprint: 2308.16406

doi: 10.48550/arXiv.2308.16406
[19]

Vijayaraghavan P, Shi L, Degan E, Mukherjee V, Zhang X. 2025. AUTOCIRCUIT-RL: reinforcement learning-driven LLM for automated circuit topology generation. Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada, 2025. Cambridge MA: JMLR. pp. 61498−61512 https://proceedings.mlr.press/v267/vijayaraghavan25a.html

[20]

Chang CC, Lin WH, Shen Y, Zhou G, Chen Y, et al. 2024. LaMAGIC: advanced circuit formulations for language-model-based topology generation for analog integrated circuits. ACM Transactions on Design Automation of Electronic Systems. 31(5):1−21

doi: 10.1145/3799428
[21]

Chang CC, Lin WH, Shen Y, Chen Y, Zhang X. 2025. LaMAGIC2: advanced circuit formulations for language model-based analog topology generation. Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada, 2025. vol. 267. Cambridge, USA: PMLR. pp. 7351−7360 . https://proceedings.mlr.press/v267/chang25b.html

[22]

Gao J, Cao W, Yang J, Zhang X. 2025. AnalogGenie: a generative engine for automatic discovery of analog circuit topologies. arXiv Preprint: 2503.00205

doi: 10.48550/arXiv.2503.00205
[23]

Chien E, Li M, Aportela A, Ding K, Jia S, et al. 2024. Opportunities and challenges of graph neural networks in electrical engineering. Nature Reviews Electrical Engineering 1(8):529−546

doi: 10.1038/s44287-024-00076-z
[24]

Chaudhuri J, Thapar D, Chaudhuri A, Firouzi F, Chakrabarty K. 2025. SPICED+: syntactical bug pattern identification and correction of trojans in A/MS circuits using LLM-enhanced detection. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 33(4):1118−1131

doi: 10.1109/TVLSI.2025.3527382
[25]

Liu C, Chen W, Peng A, Du Y, Du L, et al. 2024. AmpAgent: an LLM-based multi-agent system for multi-stage amplifier schematic design from literature for process and performance porting. arXiv Preprint: 2409.14739

doi: 10.48550/arXiv.2409.14739
[26]

Skelic L, Xu Y, Cox M, Lu W, Yu T, et al. 2025. CIRCUIT: a benchmark for circuit interpretation and reasoning capabilities of LLMs. arXiv Preprint: 2502.07980

doi: 10.48550/arXiv.2502.07980
[27]

Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, et al. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems 33 (NeurIPS 2020), online, 2020. New York: Curran Associates. pp. 1877−1901 https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf

[28]

Gao J, Cao J, Bu R, Zhu N, Guan W, et al. 2025. Promoting knowledge base question answering by directing LLMs to generate task-relevant logical forms. Proceedings of the AAAI Conference on Artificial Intelligence 39(22):23914−23922

doi: 10.1609/aaai.v39i22.34564
[29]

Wei J, Wang X, Schuurmans D, Bosma M, Xia F, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), New Orleans, 2022. New York: Curran Associates. pp. 24824-24837 https://proceedings.neurips.cc/paper_files/paper/2022/file/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf

[30]

Besta M, Blach N, Kubicek A, Gerstenberger R, Podstawski M, et al. 2024. Graph of thoughts: solving elaborate problems with large language models. Proceedings of the AAAI Conference on Artificial Intelligence 38(16):17682−17690

doi: 10.1609/aaai.v38i16.29720
[31]

Jin W, Zhao B, Yu H, Tao X, Yin R, et al. 2023. Improving embedded knowledge graph multi-hop question answering by introducing relational chain reasoning. Data Mining and Knowledge Discovery 37(1):255−288

doi: 10.1007/s10618-022-00891-8
[32]

Yu H, Wen J, Zheng Z. 2025. CAMEL: cross-modality adaptive meta-learning for text-based person retrieval. IEEE Transactions on Information Forensics and Security. 20:4651−4663

doi: 10.1109/TIFS.2025.3565392
[33]

Kambhampati S. 2024. Can large language models reason and plan? Annals of the New York Academy of Sciences 1534(1):15−18

doi: 10.1111/nyas.15125
[34]

Shojaee P, Mirzadeh I, Alizadeh K, Horton M, Bengio S, et al. 2025. The illusion of thinking: understanding the strengths and limitations of reasoning models via the lens of problem complexity. arXiv Preprint: 2506.06941

doi: 10.48550/arXiv.2506.06941
[35]

Arslan M, Ghanem H, Munawar S, Cruz C. 2024. A survey on RAG with LLMs. Procedia Computer Science 246:3781−3790

doi: 10.1016/j.procs.2024.09.178
[36]

Zhou Z, Tao R, Zhu J, Luo Y, Wang Z, et al. 2024. Can language models perform robust reasoning in chain-of-thought prompting with noisy rationales? Advances in Neural Information Processing Systems 37 (NeurIPS 2024), Vancouver, Canada, 2024. New York: Curran Associates. pp. 123846−123910 doi: 10.52202/079017-3936

[37]

Zhang C, Goh XD, Li D, Zhang H, Liu Y. 2025. Planning with multi-constraints via collaborative language agents. Proceedings of the 31st International Conference on Computational Linguistics, Abu Dhabi, 2025. Stroudsburg: ACL. pp. 10054−10082 https://aclanthology.org/2025.coling-main.672

[38]

Bhandari J, Bhat V, He Y, Rahmani H, Garg S, et al. 2024. Masala-CHAI: a large-scale SPICE netlist dataset for analog circuits by harnessing AI. arXiv Preprint:2411.14299

doi: 10.48550/arXiv.2411.14299
[39]

Tao Z, Shi Y, Huo Y, Ye R, Li Z, et al. 2024. AMSNet: netlist dataset for ams circuits. 2024 IEEE LLM Aided Design Workshop (LAD), San Jose, CA, USA, 2024. Piscataway, USA: IEEE. pp. 1−5 doi: 10.1109/LAD62341.2024.10691781

[40]

Shi Y, Zhang Z, Wang H, Tao Z, Li Z, et al. 2025. AMSbench: a comprehensive benchmark for evaluating MLLM capabilities in AMS circuits. arXiv Preprint: 2505.24138

doi: 10.48550/arXiv.2505.24138
[41]

Li M, Zhong J, Chen T, Lai Y, Psounis K. 2025. EEE-bench: a comprehensive multimodal electrical and electronics engineering benchmark. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2025. Piscataway, USA: IEEE. pp. 13337−13349 doi: 10.1109/CVPR52734.2025.01245

[42]

Jiang M, Liu KZ, Zhong M, Schaeffer R, Ouyang S, et al. 2024. Investigating data contamination for pre-training language models. arXiv Preprint: 2401.06059

doi: 10.48550/arXiv.2401.06059
[43]

Chen M, Tworek J, Jun H, Yuan Q, de Oliveira Pinto HP, et al. 2021. Evaluating large language models trained on code. arXiv Preprint: 2107.03374

doi: 10.48550/arXiv.2107.03374
[44]

Ma H, Zhang C, Bian Y, Liu L, Zhang Z, et al. 2023. Fairness-guided few-shot prompting for large language models. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), New Orleans, 2023. New York: Curran Associates. pp. 43136−43155 https://proceedings.neurips.cc/paper_files/paper/2023/file/8678da90126aa58326b2fc0254b33a8c-Paper-Conference.pdf

[45]

Renze, M. 2024. The effect of sampling temperature on problem solving in large language models. Findings of the association for computational linguistics: EMNLP 2024, Miami, Florida, USA, 2024. Stroudsburg: ACL. pp. 7346−7356 doi: 10.18653/v1/2024.findings-emnlp.432

[46]

Meurer A, Smith CP, Paprocki M, Čertík O, Kirpichev SB, et al. 2017. SymPy: symbolic computing in Python. PeerJ Computer Science 3:e103

doi: 10.7717/peerj-cs.103