The goal of this study is to increase the measurement accuracy of the bladder volume for point-of-care ultrasound (POCUS). An algorithm that can utilize spatial information from inertial measurement units (IMUs) embedded in POCUS and ultrasound images to estimate bladder volume has been developed. So far, ultrasound scanning is a noninvasive technique for treatment and diagnosis in the hospital. Bladder volume determination in post-void residual (PVR) through ultrasound can help clinicians. However, the ultrasound machines with the ability of calculating volumes precisely in hospital are bigger and expensive than POCUS. The goal of this study is to improve the accuracy of bladder volume without expensive instruments. We use an on-the-shelf wireless hand-held convex probe (LU700C, LELTEK Inc, Taiwan) to collect bladder images. LU700C is also capable of providing real-time posture information to detect User behavior. To further enhance the accuracy, an extra IMU has been attached on scanner for collecting posture data conveniently at scanning. The original prolate ellipsoid formula-based algorithm calculates bladder volume with virtual caliper. The bladder phantoms are made by ourselves to further verify the accuracy. Each of the measurement of bladders were repeat three times to follow the accepted procedural. The results show integrate hand’s posture information with timestamp into sonogram frames during bladder scanning can improve accuracy of volume estimation effectively. The proposed algorithm implements in current devices using in bladders measurement performs significantly better than the existing ones. Our goals of this research are to improve the quality of clinical through software-update without any change of hardware and to bring sustainable healthcare for areas lacking of medical resources.
KEYWORDS: Computer programming, Electron beam direct write lithography, Raster graphics, Electron beam lithography, Image compression, Logic, Detection and tracking algorithms, Semiconducting wafers, Data compression, Data processing
Data throughput is a critical metric in a multiple electron-beam direct-write (MEBDW) system so that heavy-duty data processing equipment is required. The main challenge is about how to achieve high performance with cost-effective techniques. We propose a high compression rate algorithm for efficient data transfer and high speed decompression hardware to raise data throughput of the system. The hardware decoder uses pipeline architecture, a run-length encoding first-in-first-out queue, and parallel dispatch logic to increase the throughput. The decoder is evaluated on field-programmable gate array and simulated with layout images that are compressed using the proposed compression software. The results demonstrate 18.2% better compression rate and 254.8% better throughput than the previous work with similar hardware cost. Because no static random-access memory is used in the design, the channel numbers of the system can be easily scaled up, which makes it possible for the next-generation MEBDW system to achieve higher wafer per hour targets.
As one of the critical stages of a very large scale integration fabrication process, postexposure bake (PEB) plays a crucial role in determining the final three-dimensional (3-D) profiles and lessening the standing wave effects. However, the full 3-D chemically amplified resist simulation is not widely adopted during the postlayout optimization due to the long run-time and huge memory usage. An efficient simulation method is proposed to simulate the PEB while considering standing wave effects and resolution enhancement techniques, such as source mask optimization and subresolution assist features based on the Sylvester equation and Abbe-principal component analysis method. Simulation results show that our algorithm is 20× faster than the conventional Gaussian convolution method.
Post exposure bake (PEB) Diffusion effect is one of the most difficult issues in modeling chemically amplified resists. These model equations result in a system of nonlinear partial differential equations describing the time rate of change reaction and diffusion. Verifying such models are difficult, so numerical simulations are needed to solve the model equations. In this paper, we propose a high speed 3D resist image simulation algorithm based on a novel method to solve the PEB Diffusion equation. Our major discovery is that the matrix formulation of the diffusion equation under the Crank– Nicolson scheme can be derived into a special form, AX+XB=C, where the X matrix is a 3D resist image after diffusion effect, A and B matrices contain the diffusion coefficients and the space relationship between directions x, y and z. These matrices are sparse, symmetric and diagonal dominant. The C matrix is the last time-step resist image. The Sylvester equation can be reduced to another form as (I⊗A + BT⊗I) X =C, in which the operator ⊗ is the Kronecker product notation. Compared with a traditional convolution method, our method is more useful in a way that boundary conditions can be more flexible. From our experimental results, we see that the error of the convolution method can be as high as 30% at borders of the design pattern. Furthermore, since the PEB temperature may not be uniform at multi-zone PEB, the convolution method might not be directly applicable in this scenario. Our method is about 20 times faster than the convolution method for a single time step (2 seconds) as illustrated in the attached figure. To simulate 50 seconds of the flexible PEB diffusion process, our method only takes 210 seconds with a convolution set up for a 1240×1240 working area. We use the typical 45nm immersion lithography in our work. The exposure wavelength is set to 193nm; the NA is 1.3775; and the diffusion coefficient is 1.455×10-17m2/s at PEB temperature 150°C along with PEB time 50 seconds with image resolution setup to be 1240×1240.
Resolution enhancement technologies (RETs) are so far widely
proposed in improving the quality of micro-lithography process.
Latest method such as source mask optimization (SMO)
is gaining popularity recently. Therefore, high speed simulator
is in strong demand for growing computational complexity
of RETs. In this work, we demonstrate that our
Abbe-PCA method is highly efficient for source configuring
and mask tuning using hierarchical pixel-based OPC.
Resolution enhancement technologies (RETs) are
so far widely proposed in improving the quality of
micro-lithography process. Latest methods such
as source mask optimization (SMO) and inverse
lithography technology (ILT) are gaining popularity
recently. Therefore, high speed simulator is in
strong demand for growing computational complexity
of RETs. In this paper, we demonstrate that
our previously proposed Abbe-PCA is highly efficient
for source configuring and pixel-based ILT
mask tuning.
Simultaneous source and mask optimization (SMO) has been shown to be an effective method to improve the quality of microlithography aerial imaging. However, the increasing computational complexity is also serious given that current optical proximity correction (OPC) runtime has already been very long. In this paper, we show that SMO can be done efficiently in our previous proposed Abbe-PCA method framework. Different from the Hopkins method, Abbe-PCA directly perform eigen-decomposition on the Abbe sources. In this framework, source modification is easy and efficient. Experimental results show that more than 10X runtime improvement is observed.
In the year of 1873, Professor E. Abbe, cooperating
with Carl Zeiss Inc, summarized his
discovery of the microscope imaging principle
which states that the final image is the superposition
result of all the diffracted images entering
at different angles oblique to the pupil.
This discovery forms the foundation analytical
methods to analyze optics resolving power.
Later, in 1951, the Hopkin's equations, derived
by Professor H. H Hopkins, clarified the correlation
relationship in the image from both
spatial and frequency domain. Based on Hopkin's
theory, many microlithography aerial image
simulation tools have been developed. In
this paper, we claimed that by combing Abbe's
theory with the Principle Component Analysis
(PCA) method, which is specific to the
covariance eigen-decomposition method rather
than the SVD (Singular Value Decomposition)
method, we can achieve an extremely efficient
computational algorithm to generate the essential
kernels for aerial image simulation. The
major reason for this speed up is from our discovery
that the covariance matrix of Abbe's
kernels, which is in the dimension of number
of discretized condenser sources, can be easily
constructed analytically as well as decomposed
to a basis set. As a result, an analytical
form of compact decorrelated Abbe's kernels can be obtained even without explicit formation
of the kernel images. Furthermore, the
asymptotic eigenvalues and eigenvectors of the
covariance matrix can also be obtained without
much computational effort. This discovery
also creates a new way to analyze the relationship
between sources and final images which
can be easily utilized to optimize source shape
for lithography process development. Several
imaging phenomenon have been readily explained
by this method. Extensive experimental
results demonstrate that Abbe-PCA is 10X-
40X faster than the state-of-the-art algorithm
Abbe-SVD.
To evaluate the quality of microlithography result,
massive aerial images are often generated
for careful inspection using applications such as
OPC LCC (Lithography Compaliance Check).
The number of the pixels used in a 2D aerial
image is in the order of O(n * n), where n is
the image resolution, which means the runtime
scales in a n2 fashion. However, most of
the quality indexes such as CDs or EPE (Edge
Placement Error) can be readily observed using
contours only and the number of pixels in
a specific contour is around O(n) in general.
Therefore, there is a huge waste (at least O(n))
of both computation time and memory in most
microlithography aerial image simulation tools.
The question is: "how to compute an image
contour without explicitly generate images?".
In this paper, we show that it is indeed feasible
to know the image contour with an explicit
image formation. The concept is to represent
the image in an implicit way. In our algorithm,
we utilize hierarchical region-wise function
such as 2D polynomials to fit the aerial image
kernels instead of using a bitmap type fit.
Therefore, any LUT (Look-up-table) operation
can be transformed into a polynomial look up
and mathematical operations. Since there are
only additive and subtractive operations during
aerial image generation, we only need to
apply same operations to the polynomial coefficients.
Once the LUT operation is done,
The traditional Hopkins based microlithography aerial image simulation methods most likely require expensive
4D Singular Value Decomposition operations to obtain mutually incoherent kernels. This is often a problem when
the requirement of resolution is high especially when sub-resolution assist features are present. During the process
development and model fitting, kernels are required to generate frequently. In this paper, we demonstrate that it
is not necessary to perform this expensive task to generate those kernels. By taking advantage of several classical
matrix theories, we are able to directly extract kernels without resorting to 4D Singular Value Decomposition
operations. The accuracy and efficiency will be extensively studied in the experimental results.
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