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Your search keyword '"Rosenbaum, Elyse"' showing total 15 results
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15 results on '"Rosenbaum, Elyse"'

1. Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits

Author

Yang, Alan, Xiong, Jie, Raginsky, Maxim, and Rosenbaum, Elyse

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control
Abstract

This paper proposes a class of neural ordinary differential equations parametrized by provably input-to-state stable continuous-time recurrent neural networks. The model dynamics are defined by construction to be input-to-state stable (ISS) with respect to an ISS-Lyapunov function that is learned jointly with the dynamics. We use the proposed method to learn cheap-to-simulate behavioral models for electronic circuits that can accurately reproduce the behavior of various digital and analog circuits when simulated by a commercial circuit simulator, even when interconnected with circuit components not encountered during training. We also demonstrate the feasibility of learning ISS-preserving perturbations to the dynamics for modeling degradation effects due to circuit aging.

Published
2022

7. Compact Models for Simulation of On-Chip ESD Protection Networks

Author

Rosenbaum, Elyse, Huang, Shudong, Drallmeier, Matthew, and Zhou, Yujie

Abstract

Technology scaling and increased data rates make it near impossible to achieve historic levels of electrostatic discharge (ESD) robustness. This heightens the need for pre-Si verification that a design’s ESD level is above a critical value, below which the yield loss and the number of field returns are expected to be high. Transient simulation plays a role in ESD design verification and requires the availability of accurate compact models of the various semiconductor devices, which lie along the discharge path. The compact models included in a foundry process design kit (PDK) are not accurate at ESD current levels. This article describes compact models that have been developed in the ESD device research community. It reviews the measurements used to characterize ESD protection devices and acquire data for model parameter extraction. It is concluded that obtaining accurate measurement data is challenging and this impedes the widescale adoption of ESD compact models.

Published
2024
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14. Statistical Learning of IC Models for System-Level ESD Simulation.

Author

Xiong, Jie, Chen, Zaichen, Raginsky, Maxim, and Rosenbaum, Elyse

Subjects
SIMULATION Program with Integrated Circuit Emphasis, STATISTICAL learning, ELECTROSTATIC discharges, RECURRENT neural networks, FLUX pinning, INTEGRATING circuits
Abstract

To enable accurate system-level electrostatic discharge (ESD) simulation, this article applies statistical learning to obtain I/O port models of the victim integrated circuits (ICs). A quasi-static I–V model derived using kernel regression can capture the circuit board dependency of the behavior observed at the I/O pin, regardless if there is snapback. The non-parametric kernel model can be reduced to a system-specific parametric model, which has smaller requirements for computing time and memory. In some cases, transient system-level ESD simulation may require the IC model to replicate the dynamic behavior of the nonlinear circuit. A recurrent neural network is demonstrated to be a suitable model in such cases. This article provides a detailed RNN training flow for IC pin modeling, and presents a Verilog-A implementation of the RNN for use with Simulation Program with Integrated Circuit Emphasis (SPICE)-type simulators. [ABSTRACT FROM AUTHOR]

Published
2021
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15. BETTER PERFORMANCE, HIGHER RELIABILITY, MORE SECURITY: RESEARCH HIGHLIGHTS FROM THE CENTER FOR ADVANCED ELECTRONICS THROUGH MACHINE LEARNING.

Author

Aysu, Aydin, Xu Chen, Davis, W. Rhett, Sung Kyu Lim, Franzon, Paul, Swaminathan, Madhavan, and Rosenbaum, Elyse

Subjects
*MACHINE learning, *INTEGRATED circuit design, *GENERATIVE adversarial networks, *MONTE Carlo method, *SIGNAL integrity (Electronics), *ELECTRONIC equipment
Abstract

The article reports that The Center for Advanced Electronics through Machine Learning, or CAEML, is an IndustryUniversity Cooperative Research Center (IUCRC) supported by the National Science Foundation. The IUCRC program supports pre-competitive research that is of high interest to industry. CAEML's research mission is to utilize machine learning for the design of optimized microelectronic circuits and systems and to increase the efficiency of electronic design automation.

Published
2022

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