Analysis of mixed-signal manufacturability with statistical technology CAD (TCAD)

Hanson, Deborah K.; Goossens, R.J.G.; Redford, M.; McGinty, J.; Kibarian, J.K.; Michaels, K.W.

doi:10.1109/66.542163

Cited by 18 publications

(4 citation statements)

References 10 publications

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“…This method is based on the Monte Carlo technique applied to device simulations. 4 In this method, the input parameter values are all simultaneously varied according to their individual mean and 1sigma values provided by the user. Gaussian distribution is assumed for all input parameters.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo method for highly efficient and accurate statistical lithography simulations

et al. 2002

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Recent years have shown a strong increase in the use of statistical lithography error analysis for process tuning and in making technology choices. 1 Simulation has shown it can play an important role in this area by accurately predicting experimental critical dimension (CD) distributions. Earlier statistical lithography simulation work was based on the Response Surface Methodology. The response surface is built by simulating CD dependence on input lithography process variables of interest such as focus, dose, mask CD, resist thickness, etc. The process parameters are then sampled from the Gaussian distribution to generate the distribution of the resulting resist CDs. 2,3 When a large number of input parameters are being considered in order to describe the important experimental variations, the computational runtime is rapidly increased due to the requirements to fully simulate an (N+1)-dimensional response surface, where N is the number of input parameters. The work we present here has improved the speed of statistical lithography simulations through the use of Monte Carlo technique. With this technique, the runtime of the simulations is independent of the number of input parameters. The technique can be used for 1D or 2D simulations. We present results benchmarked with 130 nm process data showing the usefulness, runtime improvements and accuracy of this method. We have also used Variable Threshold Resist model (VTRM) in conjunction with the Monte Carlo technique. VTRM was calibrated against experimental focus-exposure matrices at varying line width and pitch. The use of VTRM greatly improves the accuracy of the statistical results by the virtue of establishing a good fit to the experimental data, which can be quantified by the root mean squares of residuals. VTRM also significantly speeds up the computation, since it uses only aerial image calculation as opposed to full resist modeling. Simulation results produced by using VTRM closely match the experimental results through a range of pitches, mask line widths and various illumination conditions.

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Section: Introductionmentioning

confidence: 99%

Monte Carlo method for highly efficient and accurate statistical lithography simulations

et al. 2002

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“…Technology computer aided design (TCAD) is computer aided design and engineering used in semiconductor fabrication process design and optimization [1][2][3][4]. TCAD includes detailed simulation of all fabrication process steps.…”

Section: Introductionmentioning

confidence: 99%

A three-dimensional photoresist etching simulator for TCAD

Karafyllidis

1999

Modelling Simul. Mater. Sci. Eng.

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Cellular Automata have been extensively used as a two-dimensional simulator of the photoresist etching process. This simulator has been verified experimentally for 248 nm photolithography. In this work the Cellular Automata simulator is extended in three dimensions in order to deal with problems arising in 193 nm photolithography and beyond. The three-dimensional simulator is tested using well known etch-rate distribution functions and its results are compared with experimental results found in the literature.

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“…Technology computer aided design (TCAD) is computer aided design and engineering used in semiconductor fabrication process design and manufacturing yield optimization [1][2][3][4]. TCAD includes detailed simulation of all fabrication process steps [5,6], and it is essential for the successful design of today's devices and integrated circuits because of their increasing complexity and the increasing cost and delay of experimental design iteration [7].…”

Section: Introductionmentioning

confidence: 99%

Simulation of deposition-topography granular distortion for TCAD

Karafyllidis

Georgoulas

Hagouel

et al. 1998

Modelling Simul. Mater. Sci. Eng.

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A new deposition simulator for technology computer aided design (TCAD) is presented in this paper. The solution, on a cellular automaton lattice, of the equations that describe the deposition process has enabled the proposed simulator to handle the complicated boundary and initial conditions imposed by granular defects. This simulator can provide scanning electron microscope (SEM)-like cross-sectional views that are in very good agreement with experimental and simulation results found in the literature, and it can also be used to perform numerical experiments. The effects of granule temperature, location, and size on the deposition profiles have been studied by performing numerical experiments using this simulator.

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Analysis of mixed-signal manufacturability with statistical technology CAD (TCAD)

Cited by 18 publications

References 10 publications

Monte Carlo method for highly efficient and accurate statistical lithography simulations

Monte Carlo method for highly efficient and accurate statistical lithography simulations

A three-dimensional photoresist etching simulator for TCAD

Simulation of deposition-topography granular distortion for TCAD

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