Behavioral Modeling of Tunable I/O Drivers With Preemphasis Including Power Supply Noise

Yu, Huan; Michalka, Tim; Larbi, Mourad; Swaminathan, Madhavan

doi:10.1109/tvlsi.2019.2936815

Cited by 18 publications

(9 citation statements)

References 20 publications

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“…In the former case, the cardinality of the subsetsK for the sparse models of the various PCA coefficients varies between 29 and 132, meaning that the number of non-negligible PCE coefficients is between 0.1% and 0.5% of the total. On the other hand, the hyper-parameters of the LS-SVM regression, such as γ k,m in (24) and θ k,m in (26), are tuned based on the available set of training samples using the leave-one-out cross-validation score [31]. Figure 3 shows the variability and the standard deviation of a selection of four outputs, namely the terminal voltages on channels #0, #1, and #11, and the supply voltage of the buffer of channel #15.…”

Section: A Example 1: 16-bit Flash-memory Busmentioning

confidence: 99%

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A Data Compression Strategy for the Efficient Uncertainty Quantification of Time-Domain Circuit Responses

Manfredi

Trinchero

2020

IEEE Access

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This paper presents an innovative modeling strategy for the construction of efficient and compact surrogate models for the uncertainty quantification of time-domain responses of digital links. The proposed approach relies on a two-step methodology. First, the initial dataset of available training responses is compressed via principal component analysis (PCA). Then, the compressed dataset is used to train compact surrogate models for the reduced PCA variables using advanced techniques for uncertainty quantification and parametric macromodeling. Specifically, in this work sparse polynomial chaos expansion and least-square support-vector machine regression are used, although the proposed methodology is general and applicable to any surrogate modeling strategy. The preliminary compression allows limiting the number and complexity of the surrogate models, thus leading to a substantial improvement in the efficiency. The feasibility and performance of the proposed approach are investigated by means of two digital link designs with 54 and 115 uncertain parameters, respectively. INDEX TERMS High-speed link, least-squares support-vector machine, machine learning, polynomial chaos expansion, principal component analysis, signal integrity, uncertainty quantification. PAOLO MANFREDI (Senior Member, IEEE) received the M.Sc. degree in electronic engineering from the Politecnico di Torino, Turin, Italy, in 2009, and the Ph.D. degree in information and communication technology from the Scuola Interpolitecnica di Dottorato, Politecnico di Torino, in 2013.

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Section: A Example 1: 16-bit Flash-memory Busmentioning

confidence: 99%

“…Usually, there are two different approaches to tackle the above issue. One solution is to include the effect of both system parameters and transient dynamics into a single recursive model [24], for example via a neural network [3], [25], [26]. Such models also include the realizations at previous time points as additional input parameters.…”

Section: Introductionmentioning

confidence: 99%

A Data Compression Strategy for the Efficient Uncertainty Quantification of Time-Domain Circuit Responses

Manfredi

Trinchero

2020

IEEE Access

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“…More recently, [6] introduces special layers into their NN, namely, causality enforcement layer (CEL) and passivity enforcement layer (PEL) to enforce local causality and passivity for NN-based models. For time-domain modeling, [7]- [12] present modeling methods using nonlinear Thong Nguyen, Bobi Shi, Xu Chen, Andreas C. Cangellaris and Jose Schutt-Aine are with the University of Illinois at Urbana-Champaign, Illinois, USA, e-mail: {tnnguye3, bobishi2, xuchen1, cangella, jesa}@illinois.edu Hanzhi Ma and Er-Ping Li are with the Zhejiang University, Hangzhou, China, e-mail: {mahanzhi, liep}@zju.edu.cn autoregressive network with exogenous input type of recurrent neural network (NARX-RNN), while [13]- [17] use Elman RNN (ERNN) to model electrostastic discharge (ESD) circuits, digital high-speed link, etc. without an explicit feedback connection in the model construction.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Surrogate Modeling Methods for Signal Integrity and Microwave Circuit Applications

Nguyen

Shi

et al. 2021

IEEE Trans. Compon., Packag. Manufact. Technol.

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With a short product cycle as we see today, fast and accurate modeling methods are becoming crucial for the development of new generation of electronics devices. Furthermore, increased complexity in circuitry and integration compounds design iteration and the associated, high-dimensional sensitivity analysis and performance optimization studies. Therefore, black-box surrogate models replacing the actual circuitry offer an attractive alternative for more efficient design iteration, optimization and even direct Monte Carlo analysis. In this paper, surrogate models built using non-parametric Gaussian Process are presented. A robust framework based on probabilistic programming is used for training Gaussian Process models. Other methods such as Partial Least-Square Regression, Support Vector Regression and Polynomial Chaos are used to compare with the performance of Gaussian Process. Three design applications, a high-speed channel, a millimeter-wave filter, and a low-noise amplifier are used to demonstrate the robustness of the proposed Gaussian Process based surrogate models.

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“…A behavioral model based on input-output buffer information specifications (IBIS) or other parametric and enhanced equivalent circuit approaches can be used in SPI simulation flow that balances the tradeoff between simulation time and computational resources with good accuracy [ 2 , 3 ]. Nevertheless, previous nonlinear behavioral modelling methodologies focus mainly on improving the modelling of the last-stage of the I/O buffer [ 4 , 5 , 6 , 7 ]. In fact, voltage-time (V-t) tables capturing the predriver’s I/O timing distortions are extracted under fixed predriver’s power and ground supply voltage (PGSV)

DC voltage.…”

Section: Introductionmentioning

confidence: 99%

Neural-Network Based Modeling of I/O Buffer Predriver under Power/Ground Supply Voltage Variations

Souilem

Tripathi

Melício

et al. 2021

Sensors

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This paper presents a neural-network based nonlinear behavioral modelling of I/O buffer that accounts for timing distortion introduced by nonlinear switching behavior of the predriver electrical circuit under power and ground supply voltage (PGSV) variations. Model structure and I/O device characterization along with extraction procedure were described. The last stage of the I/O buffer is modelled as nonlinear current-voltage (I-V) and capacitance voltage (C-V) functions capturing the nonlinear dynamic impedances of the pull-up and pull-down transistors. The mathematical model structure of the predriver was derived from the analysis of the large-signal electrical circuit switching behavior. Accordingly, a generic and surrogate multilayer neural network (NN) structure was considered in this work. Timing series data which reflects the nonlinear switching behavior of the multistage predriver’s circuit PGSV variations, were used to train the NN model. The proposed model was implemented in the time-domain solver and validated against the reference transistor level (TL) model and the state-of-the-art input-output buffer information specification (IBIS) behavioral model under different scenarios. The analysis of jitter was performed using the eye diagrams plotted at different metrics values.

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Behavioral Modeling of Tunable I/O Drivers With Preemphasis Including Power Supply Noise

Cited by 18 publications

References 20 publications

A Data Compression Strategy for the Efficient Uncertainty Quantification of Time-Domain Circuit Responses

A Data Compression Strategy for the Efficient Uncertainty Quantification of Time-Domain Circuit Responses

Comparative Study of Surrogate Modeling Methods for Signal Integrity and Microwave Circuit Applications

Neural-Network Based Modeling of I/O Buffer Predriver under Power/Ground Supply Voltage Variations

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