Integrated circuit design for manufacturing through statistical simulation of process steps

Sanders, T.J.; Rekab, Kamel; Rotella, F.M.; Means, D.P.

doi:10.1109/66.175369

Cited by 28 publications

(2 citation statements)

References 4 publications

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“…-1 a(z E, T)dz' G(z, E2 , 1) = a(z,E2 , T) • t e °( 13) ct0 Surface Photon Flux E Incident Photon Energy z position When E < Eg a1 (x, EA fl = a0 • where x is afunction ofz (14) (16) 2.3.7. Intrinsic Carrier Concentration [7] The intrinsic carrier concentration is expressed as a function of band gap, temperature, and material composition.…”

Section: Carrier Generations 151mentioning

confidence: 99%

<title>New IRFPA device model</title>

Hess

Dai

Sanders

et al. 1999

Enabling Technology for Simulation Science III

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Existing infrared IRFPA models lack simplicity for setting up the detector's architecture/structure and lack continuity between JR detector material, IR detector processes, detector architecture, and detector operation. Existing models also lack the ability to reveal spatially and quantitatively the full impact of the detector's material, process and architecture on IRFPA performance. This paper discusses the development of a new IRFPA computer model used to simulate existing and future IRFPA's. This model is the first model that evaluates the IR sensor system at the device physics level and provides enhanced quantitative and visual information allowing the device engineer to determine the impact of material quality, processing procedures and LR detector architecture on IRFPA performance in the SWIR-VLWIR region. This new model is combined with powerful statistical techniques that predict IRFPA performance as well as cost. Operation under virtually unlimited user specified conditions allows the engineer to project the performance of a newly designed IRFPA prior to fabrication. The complete model provides outputs at both the device physics and detector level. When interfaced with NVESD's FLIR92 and ACQUIRE, the model provides the ability to analyze effects at the device level of the detector that impact outputs at the system level such at NETD and range.

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Section: Carrier Generations 151mentioning

confidence: 99%

<title>New IRFPA device model</title>

Hess

Dai

Sanders

et al. 1999

Enabling Technology for Simulation Science III

View full text Add to dashboard Cite

show abstract

“…For this reason design of experiments (DoE) techniques are an effective approach for selecting the parameter settings for which the models should be run to make an appropriate comparison. "Experimental design consists of purposeful changes of the inputs to a process in order to observe the corresponding changes in the outputs" [5]. A designed experiment can also be thought of as a set of experiments, runs or trials designed to maximize information and minimize experimental effort.…”

Section: Introductionmentioning

confidence: 99%

An approach for parametric and statistical comparisons of similar sensor models

Curry

2012

2012 IEEE Aerospace Conference

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Often in computer-based simulations, sensor models are used to generate realistic data to stimulate an algorithm under test. These algorithms might include, but are not limited to, guidance, navigation, control, tracking, and mission management algorithms. This paper builds on the authors' previous research to apply a systematic approach for comparing two separately developed models of the same sensor. An example application comparing two radar models is used to demonstrate the application of these methods.When working on a particular simulation or project that spans several individuals or contractors it is common for multiple models of the same sensor to be created. Multiple versions of a model may be the result of independent development, a different simulation emphasis, or due to evolution of a model over the course of a development cycle. These models may behave very differently from one another even if they are "drop-in" replacements that accept the same input format and generate the same output format. It is important for the simulation developers as well as the end users to understand the differences between two models so that they may decide to adopt or reject a new version of a model or adequately compare results generated using the two models. This motivates a need for a structured approach for comparing similar models using parametric and statistical techniques.Previous work by the authors focused on the application of design of experiments techniques (DoE) for generating model inputs, in an efficient and effective manner, that span the parameter space over which the model is designed to be valid and for which it will be used in the broader simulation environment. The results described here extend the previous approach to include parametric and statistical model comparison techniques. Monte Carlo simulations are used to generate statistical metrics for comparing the models.

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