Center Publications

B. Manna, A. Saha, Z. Jiang, K. Ni and A. Sengupta, "Variation-Resilient FeFET-Based In-Memory Computing Leveraging Probabilistic Deep Learning," in IEEE Transactions on Electron Devices, vol. 71, no. 5, pp. 2963-2969, May 2024, doi: 10.1109/TED.2024.3378223

Akinwande, O., Erdogan, S., Kumar, R., and Swaminathan, M. (2024). “Surrogate Modeling With Complex-Valued Neural Nets for Signal Integrity Applications.” IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 1, pp. 478-489, Jan. 2024, doi: 10.1109/TMTT.2023.3319835.

Aydin, F. and Aysu, A. (2024). “Leaking secrets in homomorphic encryption with side-channel attacks.” Journal of Cryptographic Engineering, volume 14, pp. 241–251, Jan. 2024, doi: 10.1007/s13389-023-00340-2

Hsiao, H., Lu, Y., Vanna-Iampikul, P. and Lim, S. (2024). “FastTuner: Transferable Physical Design Parameter Optimization using Fast Reinforcement Learning.” Proceedings of the ACM International Symposium on Physical Design, doi: 10.1145/3626184.3633328

Kashyap, P., Ravichandiran, P., Wang, L., Baron, D., Wong, C., Wu, T, and Franzon, P. (2023). “Thermal Estimation for 3D-ICs Through Generative Networks.” 2023 IEEE International 3D Systems Integration Conference (3DIC). doi: 10.1109/3DIC57175.2023.10154977.

Kashyap, P., Cheng, C., Choi, Y., and Franzon, P. (2023). “Generative Multi-Physics Models for System Power and Thermal Analysis Using Conditional Generative Adversarial Networks.” IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS). doi: 10.1109/EPEPS58208.2023.10314864. INDUSTRY CO-AUTHOR

Page, A. and Chen, X. (2023). “Machine-Learning-Based Constrained Optimization of a Test Coupon Launch Using Inverse Modeling.” IEEE 32nd Conference on Electrical Performance of Electronic Packages and Systems (EPEPS), pp. 1-3, doi: 10.1109/EPEPS58208.2023.10314941.

Yan, W., Wu, E., Schwing, A., and Rosenbaum, E. (2023). “Semantic Autoencoder for Modeling BEOL and MOL Dielectric Lifetime Distributions.” 2023 IEEE International Reliability Physics Symposium (IRPS), pp. 1-9, doi: 10.1109/IRPS48203.2023.10117878. INDUSTRY CO-AUTHOR

Molter, M., Kumar, R., Koller, S., Bhatti, O., Ambasana, N., Rosenbaum, E., and Swaminathan, M. (2023). “Thermal-Aware SoC Macro Placement and Multi-chip Module Design Optimization with Bayesian Optimization.” 2023 IEEE 73rd Electronic Components and Technology Conference (ECTC), pp. 935-942, doi: 10.1109/ECTC51909.2023.00160.

Kashyap, P., Ravichandiran, P., Baron, D., Wong, C., Wu, T., and Franzon, P. (2023). “Generative Adversarial Network Based Adaptive Transmitter Modeling.” 2023 IEEE 73rd Electronic Components and Technology Conference (ECTC), pp. 2183-2187, doi: 10.1109/ECTC51909.2023.00376.

Akinwande, O., Waqar Bhatti, O., Huang, K., Li, X. and Swaminathan, M. (2023). “Surrogate Modeling with Complex-valued Neural Nets and its Application to Design of sub-THz Patch Antenna-in-Package.” 2023 IEEE/MTT-S International Microwave Symposium. pp. 423-426, doi: 10.1109/IMS37964.2023.10187990.

Amin, F., Chatterjee, S., and Franzon, P. (2023). “DepthGraphNet: Circuit Graph Isomorphism Detection via Siamese-Graph Neural Networks.” 2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD). pp. 1-6, doi: 10.1109/MLCAD58807.2023.10299839.

A.Gajjar, P. Kashyap, A. Aysu, P. Franzon, S. Dey and C. Cheng, "FAXID: FPGA-Accelerated XGBoost Inference for Data Centers using HLS," 2022 IEEE 30th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2022, pp. 1-9, doi: 10.1109/FCCM53951.2022.9786085 INDUSTRY CO-AUTHOR

J. Xiong, A. Yang, M. Raginsky and E. Rosenbaum, "Neural Ordinary Differential Equation Models of Circuits: Capabilities and Pitfalls," in IEEE Transactions on Microwave Theory and Techniques, 2022, doi: 10.1109/TMTT.2022.3208896.

H. Ma et al., "Channel Inverse Design Using Tandem Neural Network," 2022 IEEE 26th Workshop on Signal and Power Integrity (SPI), 2022, pp. 1-3, doi: 10.1109/SPI54345.2022.9874935.

A. Page and X. Chen, "Efficient Uncertainty Quantification of Stripline Pulse Response using Singular Value Decomposition and Delay Extraction," 2022 Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC), 2022, pp. 4-6, doi: 10.1109/APEMC53576.2022.9888395.

M. Swaminathan, O. W. Bhatti, Y. Guo, E. Huang and O. Akinwande, "Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design," in IEEE Transactions on Microwave Theory and Techniques, 2022, doi: 10.1109/TMTT.2022.3206455.

A. Yang, J. Xiong, M. Raginsky, & E. Rosenbaum, "Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits," 4th Annual Conference on Learning for Dynamics and Control, Proceedings of Machine Learning Research, 2022, vol 168, p1-13, doi: 10.48550/arXiv.2202.06453

P. Kashyap et al., "Modeling of Adaptive Receiver Performance Using Generative Adversarial Networks," 2022 IEEE 72nd Electronic Components and Technology Conference (ECTC), 2022, pp. 1958-1963, doi: 10.1109/ECTC51906.2022.00307.

O. W. Bhatti, O. Akinwande and M. Swaminathan, "Uncertainty Quantification with Invertible Neural Networks for Signal Integrity Applications," 2022 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), pp. 1-4, doi: 10.1109/NEMO51452.2022.10038959.

O. Akinwande, O. W. Bhatti and M. Swaminathan, "Inverse Design of Embedded Inductor with Hierarchical Invertible Neural Transport Net," 2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), pp. 1-3, doi: 10.1109/EPEPS53828.2022.9947131.

Kashyap, P., Gajjar, A., Choi, Y., Wong, C., Baron, D., Wu, T., Cheng, C., & Franzon, P. (2022). “RxGAN: Modeling High-Speed Receiver through Generative Adversarial Networks.” MLCAD ’22: Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD. doi:10.1145/3551901.3556480

Wen, Y., Dean, J., Floyd, B. A., & Franzon, P. D. (2022). “High Dimensional Optimization for Electronic Design.” MLCAD ’22: Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD. doi: 10.1145/3551901.3556495

Akinwande, O., Bhatti, O. W., Li, X., & Swaminathan, M. (2022). “Invertible Neural Networks for Design of Broadband Active Mixers.” MLCAD ’22: Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD. doi: 10.1145/3551901.3556491

A. Page, M. Cocchini, Z. Chen and X. Chen, "Realistic Stripline Corner Modeling Using Surrogate Model and Topographic Fitting," 2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2022, pp. 1-3, doi: 10.1109/EPEPS53828.2022.9947141.

O.W. Bhatti and M. Swaminathan, “Design Space Extrapolation for Power Delivery Networks using a Transposed Convolutional Net”, 22nd International Symposium on Quality Electronic Design (ISQED’21) 2021, pp. 7-12, doi: 10.1109/ISQED51717.2021.9424309.

J. Xiong, A. S. Yang, M. Raginsky and E. Rosenbaum, "Neural Networks for Transient Modeling of Circuits : Invited Paper," 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD), 2021, pp. 1-7, doi: 10.1109/MLCAD52597.2021.9531153.

P. Kashyap, W. S. Pitts, D. Baron, C. -W. Wong, T. Wu and P. D. Franzon, "High Speed Receiver Modeling Using Generative Adversarial Networks," 2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2021, pp. 1-3, doi: 10.1109/EPEPS51341.2021.9609124.

O. W. Bhatti et al., “Comparison of Invertible Architectures for High Speed Channel Design,” 2021 IEEE Electrical Design of Advanced Packaging and Systems (EDAPS), 2021, pp. 1-3, doi: 10.1109/EDAPS53774.2021.9657014. BEST STUDENT PAPER AWARD

X-J. Shangguan, H. Ma, A. C. Cangellaris and X. Chen, “Effect of Sampling Method on the Regression Accuracy for a High-Speed Link Problem,” 2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2021, pp. 1-3, doi: 10.1109/EPEPS51341.2021.9609130.

N. Ambasana et al., “Invertible Neural Networks for High-Speed Channel Design & Parameter Distribution Estimation,” 2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2021, pp. 1-3, doi: 10.1109/EPEPS51341.2021.9609225.

O.W. Bhatti, N. Ambasana and M. Swaminathan, “Inverse Design of Power Delivery Networks using Invertible Neural Networks,” 2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2021, pp. 1-3, doi: 10.1109/EPEPS51341.2021.9609211.

Y-C. Lu, S. Nath, V. Khandelwal, and S.K. Lim, “RL-Sizer: VLSI Gate Sizing for Timing Optimization using Deep Reinforcement Learning”, 58^th ACM/IEEE Design Automation Conference (DAC), Dec 5-9, 2021. 10.1109/DAC18074.2021.9586138 INDUSTRY CO-AUTHOR

G. Aydin & E. Karabulut, & S. Potluri, & E. Alkim & A. Aysu, “RevEAL: Single-Trace Side-Channel Leakage of the SEAL Homomorphic Encryption Library”, 2022 Design, Automation and Test in Europe (DATE), December 2021.

O.W. Bhatti, H. M. Torun and M. Swaminathan, “HilbertNet: A Probabilistic Machine Learning Framework for Frequency Response Extrapolation of Electromagnetic Structures,” in IEEE Transactions on Electromagnetic Compatibility, Nov 24, 2021, p 1-13. doi: 10.1109/TEMC.2021.3119277.

J. Xiong, Z. Chen, M. Raginsky and E. Rosenbaum, “Statistical Learning of IC Models for System-Level ESD Simulation,”IEEE Transactions on Electromagnetic Compatibility, vol. 63, no. 5, pp. 1302-1311, Oct. 2021, doi: 10.1109/TEMC.2021.3076492.

L. Francisco, P. Franzon and W. R. Davis, “Fast and Accurate PPA Modeling with Transfer Learning,” 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD), Aug 30 – Sept 3, 2021, pp. 1-6, doi: 10.1109/MLCAD52597.2021.9531109.

Y-C Lu, S. Pentapati, and S. K. Lim. 2021, “The Law of Attraction: Affinity-Aware Placement Optimization using Graph Neural Networks”, Proceedings of the 2021 International Symposium on Physical Design (ISPD ‘21), Association for Computing Machinery, New York, NY, USA, 7–14. DOI:https://doi.org/10.1145/3439706.3447045

H. Huang, A. C. Cangellaris and X. Chen, “Stochastic-Galerkin Finite-Difference Time-Domain for Waves in Random Layered Media,” 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), Hangzhou, China, 2020, pp. 1-4, doi: 10.1109/NEMO49486.2020.9343635.

Y-C Lu, S. Nath, S. S. Kiran Pentapati and S. K. Lim, “A Fast Learning-Driven Signoff Power Optimization Framework,” 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), San Diego, CA, USA, 2020, pp. 1-9. INDUSTRY CO-AUTHOR

Rosenbaum, J. Xiong, A. Yang, Z. Chen and M. Raginsky, “Machine Learning for Circuit Aging Simulation,” 2020 IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2020, pp. 39.1.1-39.1.4, doi: 10.1109/IEDM13553.2020.9371931.

I. Turtletaub, M. Ibrahim, G. Li, and P. Franzon, “Application of Quantum Machine Learning to VLSI Placement,” MLCAD ’20: Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD, Virtual Event Iceland, November 2020, pp, 61-66.

L. Francisco et al., "Design Rule Checking with a CNN Based Feature Extractor," 2020 ACM/IEEE 2nd Workshop on Machine Learning for CAD (MLCAD), 2020, pp. 9-14, doi: 10.1145/3380446.3430625.

P. Kashyap, F. Aydin, S. Potluri, P. D. Franzon and A. Aysu, "2Deep: Enhancing Side-Channel Attacks on Lattice-Based Key-Exchange via 2-D Deep Learning," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 40, no. 6, pp. 1217-1229, June 2021, doi: 10.1109/TCAD.2020.3038701.

Regazzoni et al., “Machine Learning and Hardware security: Challenges and Opportunities -Invited Talk-,” 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), San Diego, CA, USA, 2020, pp. 1-6.

F. Aydin, P. Kashyap, S Potluri, P. Franzon, A. Aysu, “DeePar-SCA: Breaking Parallel Architectures of Lattice Cryptography via Learning Based Side-Channel Attacks”, International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS), October 7, 2020.

H. M. Torun, A. C. Durgun, K. Aygün and M. Swaminathan, "Causal and Passive Parameterization of S-Parameters Using Neural Networks," IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 10, pp. 4290-4304, Oct. 2020, doi: 10.1109/TMTT.2020.3011449.

M. Swaminathan, H. M. Torun, H. Yu, J. A. Hejase and W. D. Becker, "Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging," in IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 10, no. 8, pp. 1276-1295, Aug. 2020, doi: 10.1109/TCPMT.2020.3011910.

A. Yang, A-E.Ghassami, M. Raginsky, N. Kiyavash, and E. Rosenbaum, “Model-Augmented Conditional Mutual Information Estimation for Feature Selection”, Proceedings of Machine Learning Research, August 3-6, 2020.

H. Ma, E-P. Li, A.C. Cangellaris, X. Chen, “Expedient Prediction of Eye Opening of High-Speed Links with Input Design Space Dimensionality Reduction,” Proc 2020 IEEE International Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity (EMCSI), Reno, NV, July 2020, doi: 10.1109/EMCSI38923.2020.9191544

Y. -C. Lu, S. S. Kiran Pentapati, L. Zhu, K. Samadi and S. K. Lim, "TP-GNN: A Graph Neural Network Framework for Tier Partitioning in Monolithic 3D ICs," 2020 57th ACM/IEEE Design Automation Conference (DAC), 2020, pp. 1-6, doi: 10.1109/DAC18072.2020.9218582.

J. Hanson and M. Raginsky, “Universal simulation of stable dynamical systems by recurrent neural nets”, Proceedings of the 2nd Conference on Learning for Dynamics and Control (L4DC), PMLR 120:384-392, June, 2020.

H. Ma, E. -P. Li, A. C. Cangellaris and X. Chen, "Support Vector Regression-Based Active Subspace (SVR-AS) Modeling of High-Speed Links for Fast and Accurate Sensitivity Analysis," in IEEE Access, vol. 8, pp. 74339-74348, 2020, doi: 10.1109/ACCESS.2020.2988088.

A. Balakir, A. Yang and E. Rosenbaum, "An Interpretable Predictive Model for Early Detection of Hardware Failure," 2020 IEEE International Reliability Physics Symposium (IRPS), 2020, pp. 1-5, doi: 10.1109/IRPS45951.2020.9129615.

J. Hanson and M. Raginsky, “Universal approximation of input-output maps by temporal convolutional nets”, Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 8-14, 2019, p 14071-14081, doi 10.48550/14Xiv.1906.09211.

O. W. Bhatti and M. Swaminathan, "Impedance Response Extrapolation of Power Delivery Networks using Recurrent Neural Networks," 2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2019, pp. 1-3, doi: 10.1109/EPEPS47316.2019.193198.

H. M. Torun et al., "A Spectral Convolutional Net for Co-Optimization of Integrated Voltage Regulators and Embedded Inductors," 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019, pp. 1-8, doi: 10.1109/ICCAD45719.2019.8942109.

H. Ma, E. -P. Li, A. C. Cangellaris and X. Chen, "Comparison of Machine Learning Techniques for Predictive Modeling of High-Speed Links," 2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2019, pp. 1-3, doi: 10.1109/EPEPS47316.2019.193199.

H. M. Torun, A. C. Durgun, K. Aygün and M. Swaminathan, "Enforcing Causality and Passivity of Neural Network Models of Broadband S-Parameters," 2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2019, pp. 1-3, doi: 10.1109/EPEPS47316.2019.193234

Y. -C. Lu, J. Lee, A. Agnesina, K. Samadi and S. K. Lim, "GAN-CTS: A Generative Adversarial Framework for Clock Tree Prediction and Optimization," 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019, pp. 1-8, doi: 10.1109/ICCAD45719.2019.8942063.

H. Liu, Y. Zhou, A. Beirami, and D. Baron, “Nonlinear function estimation with empirical Bayes and approximate message passing,” 57^th Allerton Conf. Communication, Control, & Computing, Sep. 2019, doi 10.48550/arXiv/1907.02482.

A. Agnesina, E. Lepercq, J. Escobedo and S. K. Lim, "Reducing Compilation Effort in Commercial FPGA Emulation Systems Using Machine Learning," 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019, pp. 1-8, doi: 10.1109/ICCAD45719.2019.8942091.

A. Golder, D. Das, J. Danial, S. Ghosh, S. Sen and A. Raychowdhury, “Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, July 2019, doi 10.1109/TVLSI2019.2926324.

D. Das, A. Golder, J. Danial, S. Ghosh, A. Raychowdhury and S. Sen, "X-DeepSCA: Cross-Device Deep Learning Side Channel Attack," 2019 56th ACM/IEEE Design Automation Conference (DAC), 2019, pp. 1-6.

M. Larbi, H. M. Torun and M. Swaminathan, "Estimation of Parameter Variability for High Dimensional Microwave Problems via Partial Least Squares," 2019 IEEE MTT-S International Microwave Symposium (IMS), 2019, pp. 940-943, doi: 10.1109/MWSYM.2019.8700749.

M. A. Dolatsara, A. Varma, K. Keshavan and M. Swaminathan, "A Modified Polynomial Chaos Modeling Approach for Uncertainty Quantification," 2019 International Applied Computational Electromagnetics Society Symposium (ACES), 2019, pp. 1-2.

H. Yu, J. Shin, T. Michalka, M. Larbi and M. Swaminathan, "Behavioral Modeling of Tunable I/O Drivers with Pre-emphasis Using Neural Networks," 20th International Symposium on Quality Electronic Design (ISQED), 2019, pp. 247-252, doi: 10.1109/ISQED.2019.8697597. INDUSTRY CO-AUTHOR

M. Ahadi, J. Hejase, W. Becker, M. Swaminathan “Eye Diagram and Jitter Estimation in SerDes Designs using Surrogate Models Generated with Polynomial Chaos Theory,” DesignCon, Jan. 2019. INDUSTRY CO-AUTHOR

B. Huggins, W. R. Davis and P. Franzon, "Estimating Pareto Optimum Fronts to Determine Knob Settings in Electronic Design Automation Tools," 20th International Symposium on Quality Electronic Design (ISQED), 2019, pp. 304-310, doi: 10.1109/ISQED.2019.8697576.

M. Ahadi Dolatsara, J. A. Hejase, W. D. Becker and M. Swaminathan, "A Hybrid Methodology for Jitter and Eye Estimation in High-Speed Serial Channels Using Polynomial Chaos Surrogate Models," IEEE Access, vol. 7, pp. 53629-53640, 2019, doi: 10.1109/ACCESS.2019.2908799. INDUSTRY CO-AUTHOR

B. Tzen and M.Raginsky, “Theoretical Guarantees for Sampling and Inference in Generative Models with Latent Diffusions”, COLT 2019, doi 10.48550/arXiv.1903.01608.

A. Yang, A. Ghassami, E. Rosenbaum, and N. Kiyavash, “Data-Driven Reliability for Datacenter Hard Disk Drives,” Electronic Device Failure Analysis Magazine, vol. 21, no. 2, May 2019, pp. 16-21.

E. Rosenbaum, Z. Chen, J. Xiong, “Component Modeling for System-level ESD Simulation,” InCompliance, vol. 11, no. 4, April 2019, pp. 16-19

M. Ahadi, A. Varma, K. Keshavan, M. Swaminathan “Design Space Exploration with Polynomial Chaos Surrogate Models for Analyzing Large System Designs,” DesignCon, Jan. 2019.

K. Roy, H. T. Mert and M. Swaminathan, "Preliminary Application of Deep Learning to Design Space Exploration," 2018 IEEE Electrical Design of Advanced Packaging and Systems Symposium (EDAPS), 2018, pp. 1-3, doi: 10.1109/EDAPS.2018.8680888.

D. Das, S. Maity, S. B. Nasir, S. Ghosh, A. Raychowdhury and S. Sen, "ASNI: Attenuated Signature Noise Injection for Low-Overhead Power Side-Channel Attack Immunity," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 65, no. 10, pp. 3300-3311, Oct. 2018, doi: 10.1109/TCSI.2018.2819499

H. M. Torun and M. Swaminathan, "Bayesian Framework for Optimization of Electromagnetics Problems," 2018 International Workshop on Computing, Electromagnetics, and Machine Intelligence (CEMi), 2018, pp. 1-2, doi: 10.1109/CEMI.2018.8610600.

H. M. Torun and M. Swaminathan, “A New Machine Learning Approach for Optimization and Tuning of Integrated Systems”, DesignCon, Santa Clara, CA, 2018.

B. Li, P. Franzen, Y. Choi and C. Cheng, "Receiver Behavior Modeling Based on System Identification," 2018 IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2018, pp. 299-301, doi: 10.1109/EPEPS.2018.8534310. INDUSTRY CO-AUTHOR

M. A. Dolatsara, H. Yu, J. A. Hejase, W. D. Becker and M. Swaminathan, "Polynomial Chaos modeling for jitter estimation in high-speed links," 2018 IEEE International Test Conference (ITC), 2018, pp. 1-10, doi: 10.1109/TEST.2018.8624875. INDUSTRY CO-AUTHOR

H. Yu, H. Chalamalasetty and M. Swaminathan, "Behavioral Modeling of Steady-State Oscillators with Buffers Using Neural Networks," 2018 IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2018, pp. 307-309, doi: 10.1109/EPEPS.2018.8534238.

H. M. Torun, J. A. Hejase, J. Tang, W. D. Beckert and M. Swaminathan, "Bayesian Active Learning for Uncertainty Quantification of High Speed Channel Signaling," 2018 IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2018, pp. 311-313, doi: 10.1109/EPEPS.2018.8534251. INDUSTRY CO-AUTHOR

Y. Wang and P. D. Franzon, "RFIC IP Redesign and Reuse Through Surrogate Based Machine Learning Method," 2018 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), 2018, pp. 1-4, doi: 10.1109/NEMO.2018.8503446.

J. Xiong, Z. Chen, Y. Xiu, Z. Mu, M. Raginsky and E. Rosenbaum, "Enhanced IC Modeling Methodology for System-level ESD Simulation," 2018 40th Electrical Overstress/Electrostatic Discharge Symposium (EOS/ESD), 2018, pp. 1-10, doi: 10.23919/EOS/ESD.2018.8509751.

T. Nguyen and J. E. Schutt-Aine, "A pseudo-supervised machine learning approach to broadband LTI macro-modeling," 2018 IEEE International Symposium on Electromagnetic Compatibility and 2018 IEEE Asia-Pacific Symposium on Electromagnetic Compatibility (EMC/APEMC), 2018, pp. 1018-1021, doi: 10.1109/ISEMC.2018.8393939.

H. Torun & M. Swaminathan. Artificial Intelligence and Its Impact on System Design, IEEE Electronic Components and Technology Conference (ECTC), San Diego, CA, May 29 – June 1, 2018.

H. M. Torun, M. Swaminathan, A. Kavungal Davis and M. L. F. Bellaredj, "A Global Bayesian Optimization Algorithm and Its Application to Integrated System Design," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 26, no. 4, pp. 792-802, April 2018, doi: 10.1109/TVLSI.2017.2784783.

X. Ma, M. Raginsky and A. C. Cangellaris, "A machine learning methodology for inferring network S-parameters in the presence of variability," 2018 IEEE 22nd Workshop on Signal and Power Integrity (SPI), 2018, pp. 1-4, doi: 10.1109/SaPIW.2018.8401643.

Y. Xiu, S. Sagan, A. Battini, X. Ma, M. Raginsky and E. Rosenbaum, "Stochastic modeling of air electrostatic discharge parameters," 2018 IEEE International Reliability Physics Symposium (IRPS), 2018, pp. 2C.2-1-2C.2-10, doi: 10.1109/IRPS.2018.8353548.

H. Yu, M. Swaminathan, C. Ji and D. White, "A Nonlinear Behavioral Modeling Approach for Voltage-controlled Oscillators Using Augmented Neural Networks," 2018 IEEE/MTT-S International Microwave Symposium - IMS, 2018, pp. 551-554, doi: 10.1109/MWSYM.2018.8439324

E. J. Wyers, W. Qi and P. D. Franzon, "A robust calibration and supervised machine learning reliability framework for digitally-assisted self-healing RFICs," 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), 2017, pp. 1138-1141, doi: 10.1109/MWSCAS.2017.8053129.

T. Nguyen, J. E. Schutt-Aine and Y. Chen, "Volterra kernels extraction from frequency-domain data for weakly non-linear circuit time-domain simulation," 2017 IEEE Radio and Antenna Days of the Indian Ocean (RADIO), 2017, pp. 1-2, doi: 10.23919/RADIO.2017.8242244.

Z. Chen, M. Raginsky and E. Rosenbaum, "Verilog-A compatible recurrent neural network model for transient circuit simulation," 2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2017, pp. 1-3, doi: 10.1109/EPEPS.2017.8329743.

H. Yu, M. Swaminathan, C. Ji and D. White, "A method for creating behavioral models of oscillators using augmented neural networks," 2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2017, pp. 1-3, doi: 10.1109/EPEPS.2017.8329714.

S. J. Park, B. Bae, J. Kim and M. Swaminathan, "Application of Machine Learning for Optimization of 3-D Integrated Circuits and Systems," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 25, no. 6, pp. 1856-1865, June 2017, doi: 10.1109/TVLSI.2017.2656843.

B. Li and P. D. Franzon, "Machine learning in physical design," 2016 IEEE 25th Conference on Electrical Performance Of Electronic Packaging And Systems (EPEPS), 2016, pp. 147-150, doi: 10.1109/EPEPS.2016.7835438.