A new TCAD-based statistical methodology for the optimization and sensitivity analysis of semiconductor technologies

Williams, S.C.; Varahramyan, Kody

doi:10.1109/66.843636

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…Even concerning the active device models, a very limited simulation capability is nowadays available at the EDA level to model PIV [12,13], even if recent developments demonstrate the interest for this topic [14]. As for passive structures, physics-based simulations through calibrated technology CAD (TCAD) would represent the ideal framework to incorporate PIV into microwave design, but EDA tools do not allow for co-simulation of the active device physical model into the circuit-level design flow, mainly due to the numerical burden of the nonlinear physical model (e.g., the drift-diffusion model) solution.…”

Section: Introductionmentioning

confidence: 99%

Bridging the Gap between Physical and Circuit Analysis for Variability-Aware Microwave Design: Modeling Approaches

et al. 2022

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Process-induced variability is a growing concern in the design of analog circuits, and in particular for monolithic microwave integrated circuits (MMICs) targeting the 5G and 6G communication systems. The RF and microwave (MW) technologies developed for the deployment of these communication systems exploit devices whose dimension is now well below 100 nm, featuring an increasing variability due to the fabrication process tolerances and the inherent statistical behavior of matter at the nanoscale. In this scenario, variability analysis must be incorporated into circuit design and optimization, with ad hoc models retaining a direct link to the fabrication process and addressing typical MMIC nonlinear applications like power amplification and frequency mixing. This paper presents a flexible procedure to extract black-box models from accurate physics-based simulations, namely TCAD analysis of the active devices and EM simulations for the passive structures, incorporating the dependence on the most relevant fabrication process parameters. We discuss several approaches to extract these models and compare them to highlight their features, both in terms of accuracy and of ease of extraction. We detail how these models can be implemented into EDA tools typically used for RF and MMIC design, allowing for fast and accurate statistical and yield analysis. We demonstrate the proposed approaches extracting the black-box models for the building blocks of a power amplifier in a GaAs technology for X-band applications.

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Section: Introductionmentioning

confidence: 99%

Bridging the Gap between Physical and Circuit Analysis for Variability-Aware Microwave Design: Modeling Approaches

et al. 2022

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“…The statistical variations of wire delay and crosstalk glitch amplitude are obtained using design for experiment technique (DoE). This simulation based approach has already been used in [6][7][8] for modeling variability of device and circuit parameters due to its high computation efficiency compared to the traditional Monte Carlo analysis. Using these statistics we are able to develop analytical expressions to estimate the probability of erroneous transmission of a data bit through a global interconnect wire.…”

Section: Introductionmentioning

confidence: 99%

The impact of variability on the reliability of long on-chip interconnect in the presence of crosstalk

Halak

Shedabale

Ramakrishnan

et al. 2008

Proceedings of the 2008 International Workshop on System Level Interconnect Prediction

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With deep submicron technologies, the importance of interconnect parasitics on delay and noise has been an ever increasing trend. Consequently the variation in interconnect parameters have a larger impact on final timing and functional yield of the product.We present a comprehensive analysis to quantify the impact of parametric variations on the reliability of global interconnect links in the presence of crosstalk. The impact of parametric variations on wire delay and crosstalk noise is studied for a global interconnect structure in 90nm UMC technology, followed by a novel technique to estimate the bit error rate (BER) of such links. This methodology is employed to explore the design space of interconnect channels in order to mitigate the impact of variability.

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“…However, the computational load remains very high and analytic functions developed to serve as surrogate models are best suited for a cost-effective study. Several such integration schemes with different methods and scopes of operation have been proposed [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Using TCAD, Response Surface Model and Monte Carlo Methods to Model Processes and Reduce Device Variation

Basu¹,

Guha

Hatab

et al. 2009

2009 International Conference on Simulation of Semiconductor Processes and Devices

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Reduction of electrical parameter variation is essential to achieve high yield and reliability in semiconductor devices. However, variation depends on a large number of process factors, which are often interdependent. In this work, well-calibrated Technology Computer-Aided-Design process and device simulations were performed in a designed experiment to develop an efficient, surrogate response surface model (RSM) of the device parameters as a function of key process factors. Monte Carlo simulations were performed with the RSM to estimate variation and design systems to reduce variation. The approach, illustrated here specifically for peripheral n-type field-effect transistors in a dynamic random-access-memory process flow, is general, easy-to-implement, and a cost-effective way to systematically identify, model, and analyze process variation.

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A new TCAD-based statistical methodology for the optimization and sensitivity analysis of semiconductor technologies

Cited by 12 publications

References 9 publications

Bridging the Gap between Physical and Circuit Analysis for Variability-Aware Microwave Design: Modeling Approaches

Bridging the Gap between Physical and Circuit Analysis for Variability-Aware Microwave Design: Modeling Approaches

The impact of variability on the reliability of long on-chip interconnect in the presence of crosstalk

Using TCAD, Response Surface Model and Monte Carlo Methods to Model Processes and Reduce Device Variation

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