Netlisting and Modeling Well-Proximity Effects

Watts, J.; Su, Kuan-Wei; Basel, M.

doi:10.1109/ted.2006.880176

Cited by 22 publications

(7 citation statements)

References 3 publications

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“…They enable the use of specially parameterized and formulated expressions to implement the desired statistical model behavior (Lu et al, 2009). Recent compact models are also incorporating new parameters that, when combined with extracted layout information, can better predict important mismatch sensitivities, such as stress and well proximity effects (Watts et al, 2006;Yang et al, 2008). We have implemented our parameterized statistical models within the Cadence Analog Design Environment, utilizing the monte carlo features available within their Statistical Analysis Tool (Potts & Luk, 2005).…”

Section: Implementing Statistical Modelsmentioning

confidence: 99%

Statistical Analog Circuit Simulation: Motivation and Implementation

Potts¹

2011

Advances in Analog Circuits

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Section: Implementing Statistical Modelsmentioning

confidence: 99%

Statistical Analog Circuit Simulation: Motivation and Implementation

Potts¹

2011

Advances in Analog Circuits

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“…In general as the distance between the gate and the well boundary grows smaller, the well region beneath the gate gains a greater amount of dopant atoms and hence has a higher threshold voltage. Another, more recently recognized component, of WPE is pocket shadowing which means the full pocket dose does not enter the silicon but a portion of the pocket dose is implanted into the photoresist or is "shadowed" [6]. This effect will lower the dose of dopant atoms available to increase the threshold voltage and will thus reduce the threshold voltage.…”

Section: Well Proximity Effectmentioning

confidence: 99%

“…This effect will lower the dose of dopant atoms available to increase the threshold voltage and will thus reduce the threshold voltage. In general to model this effect, all the well edges within a 1 -2 micron region (process dependent) need to be taken into account and the overall voltage shift from these edges summed together to reach a total voltage shift [6]. As shown in Figure 8, the general threshold voltage shift as a function of distance from the well edge is captured.…”

Section: Well Proximity Effectmentioning

confidence: 99%

Context Dependence Effects in Si/SiON Based Advanced CMOS Devices

Olubuyide

2011

ECS Trans.

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a Texas Instruments, Inc.; MS 366, 13121 TI Blvd., Dallas, TX 75243 Significant sources of mechanical stress have been deliberately added into the active silicon of CMOS devices to improve carrier mobility. These include Dual Stress nitride Liners (DSL) and embedded Silicon Germanium (eSiGe) in the source and drain regions of pMOS transistors. Moreover, as transistor dimensions have shrunk, unintentional sources of variation such as Shallow Trench Isolation (STI) mechanical stress and dopant redistribution due to proximity to STI boundary or the well implant boundary (WPE) have also increased the systematic transistor variability. This increased systematic variability has impacted the product yield and provided the impetus to include context dependence in SPICE models of transistor electrical performance. This paper outlines a rigorous approach to not only quantify the physical mechanisms and impact of context effects, but also how to model these effects on the electrical transistor performance and various strategies to mitigate the impact of these context dependencies on digital circuit performance.

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“…Reference [7] discusses some of the challenge in collecting information about the environment of a device. First is the simple matter of a large number of shapes to examine for each device.…”

Section: Future Challengesmentioning

confidence: 99%

Modeling circuit variability

Watts

2007

2007 International Workshop on Physics of Semiconductor Devices

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In order to design integrated circuits which can be manufactured with high yield the variations which can occur during manufacturing must be included with the compact models. The manufacturing variations comprise a complex correlated set of statistical distributions. This paper presents some of the current options and challenges in modeling variation.Improving the prediction of statistical circuit behavior will require coordinated improvement in models, netlisting tools and simulators.

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Netlisting and Modeling Well-Proximity Effects

Cited by 22 publications

References 3 publications

Statistical Analog Circuit Simulation: Motivation and Implementation

Statistical Analog Circuit Simulation: Motivation and Implementation

Context Dependence Effects in Si/SiON Based Advanced CMOS Devices

Modeling circuit variability

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