The phase-shifting point diffraction interferometer (PS/PDI) is so far the most accurate measurement tool in atwavelength
interferometry of projection optics for extreme ultraviolet lithography (EUVL). The complicate
interrelationships between configuration parameters of PS/PDI call for an optimization to achieve high accuracy of
PS/PDI. In this paper, a novel system-level modeling approach is proposed to optimize the parameters of PS/PDI
designed for visible light (λ=632.8nm) concept proof experiment. The optimal reference pinhole size selection is
performed by modeling pinhole spatial filtering effect using Diffraction-Based Beam Propagation (BPR) module of
CODE V and in house software. The result shows that various pinhole diameters ranging from 1.6um to 2.2um should be
used in our PS/PDI experiment. The test window size and grating duty cycle optimization, which is based on the spatial
frequency domain analysis of PS/PDI, is conducted by modeling the entire PS/PDI system using Physical Optics
Propagation (POP) module of Zemax and in house software. The optimal window size is approximately 62um for a
given window-pinhole separation of 63.3um. The optimal duty cycle of grating is calculated to be 83% to obtain the
maximum fringe contrast of 0.879.
An effective computer aided alignment (CAA) scheme based on the singular value decomposition (SVD) of sensitivity matrix is used in the alignment of a set of 20X Schwarzschild projection optics. Alignment simulation is conducted by ray tracing software CODE V to verify the convergence of CAA method within the calculated tolerance limit. The result shows that, with the misalignment value calculated by CAA, fine alignment could be achieved after only one adjustment step. Alignment experiment is also performed according to the CAA scheme. After fine alignment, the measured wavefront error (WFE) is 18nm in rms, which is consistent with the CODE V simulation result in ideal alignment status.
The presence of noise in interferograms is unavoidable, it may be introduced in acquisition and transmission. These
random distortions make it difficult to perform any required processing. Removing noise is often the first step in
interferograms analysis. In recent yeas, partial differential equations(PDEs) method in image processing have received
extensive concern. compared with traditional approaches such as median filter, average filter, low pass filter etc, PDEs
method can not only remove noise but also keep much more details without blurring or changing the location of the
edges. In this paper, a fourth-order partial differential equation was applied to optimize the trade-off between noise
removal and edges preservation. The time evolution of these PDEs seeks to minimize a cost function which is an
increasing function of the absolute value of the Laplacian of the image intensity function. Since the Laplacian of an
image at a pixel is zero if the image is planar in its neighborhood. these PDEs attempt to remove noise and preserve
edges by approximating an observed image with a piecewise planar image .piecewise planar images look more nature
than step images which anisotropic diffusion (second order PDEs)uses to approximate an observed image .The
simulation results make it clear that the fourth-order partial differential equatoin can effectively remove noise and
preserve interferogram edges.
Fast Fourier Transform (FFT) is one of the most important interferogram analysis methods with the merits of single interferogram captured, low experimental environment requirements and better accuracy. FFT arithmetic can only process numeric discrete data and requires that the number of line and row pixels must be 2 to the power n. But unfortunately, interferograms captured are circular in general. The fringe extrapolation method has been proved to be very effective in avoiding Gibbs phenomenon and reducing phase evaluation errors. Gerchberg proposed a simple iterative algorithm to extrapolate the interferograms, but there is not a good evaluation criterion of the iterative times. In this paper, a method of exemplar-based image inpainting is proposed. First, the priority of each patch on the "fill front" should be calculated. We define its priority <i>P(p)</i> as the product of two terms <i>p(p)=C(p)D(p).C(p)</i> is the confidence term and <i>D(p)</i> is the data term. The patch with the highest priority is obtained. Then, the best exemplar patch corresponding to the patch with the highest priority is discovered in the exemplar region. And the relevant data is copied from the best exemplar patch to the unfilled parts of the patch with the highest priority. Finally, the confidence values of pixels which have been filled just now are updated. The whole region will be fully filled through iterating the above steps. Computer simulation and experiment make it clear that the proposed algorithm extrapolate the texture and structure information effectually.