Three-dimensional deep sub-wavelength defect detection using λ = 193 nm optical microscopy.

Optical microscopy is sensitive both to arrays of nanoscale features and to their imperfections. Optimizing scattered electromagnetic field intensities from deep sub-wavelength nanometer scale structures represents an important element of optical metrology. Current, well-established optical methods used to identify defects in semiconductor patterning are in jeopardy by upcoming sub-20 nm device dimensions. A novel volumetric analysis for processing focus-resolved images of defects is presented using simulated and experimental examples. This new method allows defects as narrow as (16 ± 2) nm (k = 1) to be revealed using 193 nm light with focus and illumination conditions optimized for three-dimensional data analysis. Quantitative metrics to compare two-dimensional and three-dimensional imaging indicate possible fourfold improvements in sensitivity using these methods.

Fourier domain optical tool normalization for quantitative parametric image reconstruction.

There has been much recent work in developing advanced optical metrology methods that use imaging optics for critical dimension measurements and defect detection. Sensitivity to nanometer-scale changes has been observed when measuring critical dimensions of subwavelength 20 nm features or when imaging defects below 15 nm using angle-resolved and focus-resolved optical data. However, these methods inherently involve complex imaging optics and analysis of complicated three-dimensional electromagnetic fields. This paper develops a new approach to enable the rigorous analysis of three-dimensional, through-focus, or angle-resolved optical images. We use rigorous electromagnetic simulation with enhanced Fourier optical techniques, an approach to optical tool normalization, and statistical methods to evaluate sensitivities and uncertainties in the measurement of subwavelength three-dimensional structures.

Computational micromodel for epigenetic mechanisms.

Characterization of the epigenetic profile of humans since the initial breakthrough on the human genome project has strongly established the key role of histone modifications and DNA methylation. These dynamic elements interact to determine the normal level of expression or methylation status of the constituent genes in the genome. Recently, considerable evidence has been put forward to demonstrate that environmental stress implicitly alters epigenetic patterns causing imbalance that can lead to cancer initiation. This chain of consequences has motivated attempts to computationally model the influence of histone modification and DNA methylation in gene expression and investigate their intrinsic interdependency. In this paper, we explore the relation between DNA methylation and transcription and characterize in detail the histone modifications for specific DNA methylation levels using a stochastic approach.