2,048 research outputs found
Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition
Hierarchical uncertainty quantification can reduce the computational cost of
stochastic circuit simulation by employing spectral methods at different
levels. This paper presents an efficient framework to simulate hierarchically
some challenging stochastic circuits/systems that include high-dimensional
subsystems. Due to the high parameter dimensionality, it is challenging to both
extract surrogate models at the low level of the design hierarchy and to handle
them in the high-level simulation. In this paper, we develop an efficient
ANOVA-based stochastic circuit/MEMS simulator to extract efficiently the
surrogate models at the low level. In order to avoid the curse of
dimensionality, we employ tensor-train decomposition at the high level to
construct the basis functions and Gauss quadrature points. As a demonstration,
we verify our algorithm on a stochastic oscillator with four MEMS capacitors
and 184 random parameters. This challenging example is simulated efficiently by
our simulator at the cost of only 10 minutes in MATLAB on a regular personal
computer.Comment: 14 pages (IEEE double column), 11 figure, accepted by IEEE Trans CAD
of Integrated Circuits and System
Yield Enhancement of Digital Microfluidics-Based Biochips Using Space Redundancy and Local Reconfiguration
As microfluidics-based biochips become more complex, manufacturing yield will
have significant influence on production volume and product cost. We propose an
interstitial redundancy approach to enhance the yield of biochips that are
based on droplet-based microfluidics. In this design method, spare cells are
placed in the interstitial sites within the microfluidic array, and they
replace neighboring faulty cells via local reconfiguration. The proposed design
method is evaluated using a set of concurrent real-life bioassays.Comment: Submitted on behalf of EDAA (http://www.edaa.com/
Continuation-Based Pull-In and Lift-Off Simulation Algorithms for Microelectromechanical Devices
The voltages at which microelectromechanical actuators and sensors become unstable, known as pull-in and lift-off voltages, are critical parameters in microelectromechanical systems (MEMS) design. The state-of-the-art MEMS simulators
compute these parameters by simply sweeping the voltage, leading to either excessively large computational cost or to convergence failure near the pull-in or lift-off points. This paper proposes to simulate the behavior at pull-in and lift-off employing
two continuation-based algorithms. The first algorithm appropriately adapts standard continuation methods, providing a complete set of static solutions. The second algorithm uses continuation to trace two kinds of curves and generates the sweep-up or sweep-down curves, which can provide more intuition for MEMS designers. The algorithms presented in this paper are robust and suitable for general-purpose industrial MEMS designs. Our algorithms have been implemented in a commercial MEMS/integrated circuits codesign tool, and their effectiveness is validated by comparisons against measurement data and the commercial finite-element/boundary-element (FEM/BEM) solver CoventorWare
Multi-Domain Fault Models Covering the Analog Side of a Smart or Cyber-Physical System
Over the last decade, the industrial world has been involved in a massive revolution guided by the adoption of digital technologies. In this context, complex systems like cyber-physical systems play a fundamental role since they were designed and realized by composing heterogeneous components. The combined simulation of the behavioral models of these components allows to reproduce the nominal behavior of the real system. Similarly, a smart system is a device that integrates heterogeneous components but in a miniaturized form factor. The development of smart or cyber-physical systems, in combination with faulty behaviors modeled for the different physical domains composing the system, enables to support advanced functional safety assessment at the system level. A methodology to create and inject multi-domain fault models in the analog side of these systems has been proposed by exploiting the physical analogy between the electrical and mechanical domains to infer a new mechanical fault taxonomy. Thus, standard electrical fault models are injected into the electrical part, while the derived mechanical fault models are injected directly into the mechanical part. The entire flow has been applied to two case studies: a direct current motor connected with a gear train, and a three-axis accelerometer
Distributed Intelligent MEMS: Progresses and Perspectives
International audienceMEMS research has until recently focused mainly on the engineering process, resulting in interesting products and a growing market. To fully realize the promise of MEMS, the next step is to add embedded intelligence. With embedded intelligence, the scalability of manufacturing will enable distributed MEMS systems consisting of thousands or millions of units which can work together to achieve a common goal. However, before such systems can become a reallity, we must come to grips with the challenge of scalability which will require paradigm-shifts both in hardware and software. Furthermore, the need for coordinated actuation, programming, communication and mobility management raises new challenges in both control and programming. The objective of this article is to report the progresses made by taking the example of two research projects and by giving the remaining challenges and the perspectives of distributed intelligent MEMS
Calculation of Generalized Polynomial-Chaos Basis Functions and Gauss Quadrature Rules in Hierarchical Uncertainty Quantification
Stochastic spectral methods are efficient techniques for uncertainty
quantification. Recently they have shown excellent performance in the
statistical analysis of integrated circuits. In stochastic spectral methods,
one needs to determine a set of orthonormal polynomials and a proper numerical
quadrature rule. The former are used as the basis functions in a generalized
polynomial chaos expansion. The latter is used to compute the integrals
involved in stochastic spectral methods. Obtaining such information requires
knowing the density function of the random input {\it a-priori}. However,
individual system components are often described by surrogate models rather
than density functions. In order to apply stochastic spectral methods in
hierarchical uncertainty quantification, we first propose to construct
physically consistent closed-form density functions by two monotone
interpolation schemes. Then, by exploiting the special forms of the obtained
density functions, we determine the generalized polynomial-chaos basis
functions and the Gauss quadrature rules that are required by a stochastic
spectral simulator. The effectiveness of our proposed algorithm is verified by
both synthetic and practical circuit examples.Comment: Published by IEEE Trans CAD in May 201
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