Experimental study of Gummel-Poon model parameter correlations for bipolar junction transistors

Divekar, D.; Dutton, R.W.; McCalla, William J.

doi:10.1109/jssc.1977.1050954

Cited by 23 publications

(2 citation statements)

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“…For example, channel and field threshold adjustments involving ion implantation, diffusion and oxidation supported design optimizations of the then dominant enhancement/depletion (E/D) NMOS technology [1]. While sheet resistances and minority carrier effects could not be predictively evaluated, TCAD still played a highly productive role in the design of bipolar transistors (BJTs) and first-generation laterally doped (LD) MOS, providing means to understand the process trade-offs and parameter interdependencies [2], [3]. The need for calibration of TCAD was quickly realized as essential in augmenting the physically based models.…”

Section: Technology Scaling and Evolution Of Tcadmentioning

confidence: 99%

Perspectives on technology and technology-driven CAD

Dutton

Strojwas

2000

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-Computer-aided design (CAD) techniques are absolutely essential to harness the ever-increasing complexity of the microsystem design. Similarly, the technology CAD (TCAD) tools played a key role in the development of new technology generations. Although there is a common belief that the TCAD tools have been trailing the technology development, the situation has been changing very significantly especially over the last decade. For the deep submicrometer (DSM) devices, these tools provide a better insight than any measurement techniques and they have become indispensable in the new device creation. Moreover, these tools after calibration to a relatively small number of experiments, exhibit very impressive predictive power, which is utilized to speed up the technology integration and transfer to volume manufacturing. This results in very manufacturable high-yielding products that can be ramped up much faster than in the past decade, which is absolutely necessary given the huge costs of integrated circuit fabrication lines, short product lifecycles and penalties for being late to the market place. In this paper, we will present our perspective on the semiconductor technology development, and highlight the rapid growth of TCAD and its strategic use in semiconductor industry.

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Section: Technology Scaling and Evolution Of Tcadmentioning

confidence: 99%

Perspectives on technology and technology-driven CAD

Dutton

Strojwas

2000

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…A plot of the measured variation of maximum current gain versus I ( Fig. The results show that both dc and ac parameters are accurately modeled based on the dominant statistical variable, the base transport current [27]. Based on similar plots of data and using regression analysis to fit straight lines, the equations given in Table II are obtained [27].…”

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confidence: 89%

Modeling of the Silicon Integrated-Circuit Design and Manufacturing Process

Dutton¹

1983

SIAM J. Sci. and Stat. Comput.

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The evolution of process modeling is traced starting with bipolar technology in the 1960's through recent processing concerns for oxide-isolated MOS devices. The kinetics of diffusion and oxidation are used to illustrate both physical and numerical effects. The interaction of device effects with process modeling is discussed as well as the statistical implications of process variables. The nature of computer-aided design tools for process and device modeling are discussed. This includes tools that bridge gaps between technology and system design with potential application for manufacturing.

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Statistical Modeling and Worst-Case Design Parameters

Arora

1993

Computational Microelectronics

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Experimental study of Gummel-Poon model parameter correlations for bipolar junction transistors

Cited by 23 publications

References 2 publications

Perspectives on technology and technology-driven CAD

Perspectives on technology and technology-driven CAD

Modeling of the Silicon Integrated-Circuit Design and Manufacturing Process

Statistical Modeling and Worst-Case Design Parameters

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