&lt;title&gt;Enhanced optimization algorithms for the development of microsystems&lt;/title&gt;

Peters, D.; Bolte, Hilmar; Marschner, Claudia; Nuessen, Oliver; Laur, R.

doi:10.1117/12.425382

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…This algorithm is generally used in computation and search algorithms [19,20], specifically cost minimization [21], real-world modeling [22], and artificial intelligence [23]. Optimization algorithms are seldom used in the field of MEMS for device operation, with most examples in design parameter optimization [24] and device calibration [25].…”

Section: Hill-climbing Algorithmmentioning

confidence: 99%

An adaptive feedback circuit for MEMS resonators

Fan

Siwak

Ghodssi

2011

J. Micromech. Microeng.

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The first adaptive feedback circuit capable of detecting resonant frequencies for a wide range of MEMS resonators is presented. The feedback system presented implements a hill-climbing algorithm that sweeps actuation frequencies, locking onto the resonance condition at maximum cantilever amplitude response without limitations on the frequency range. To demonstrate its adaptability, a circuit implementation of this feedback algorithm was used to detect the resonant frequency of eight different cantilever-based sensors (width (W) = 1.4 μm, length (L) = 40-75 μm, and thickness (T) = 1.8 μm), resonating at 201.0 to 592.1 kHz. Additionally, the same circuit was used to track resonant frequency shifts due to isopropanol adsorption on three different chemical sensors with no modifications. The feedback electronics integrated with these resonator sensors provide a mass resolution limit of 123 femptograms. The realization of this system will enable real-time chip-scale sensor systems, providing an alternative to external instrumentation modules that perform sensor control and monitoring.

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Section: Hill-climbing Algorithmmentioning

confidence: 99%

An adaptive feedback circuit for MEMS resonators

Fan

Siwak

Ghodssi

2011

J. Micromech. Microeng.

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“…Rosenbrock's strategy [7] RCT algorithm [11] [8] Complex strategy with modifications after Guin [9] [10] Combined polyeder strategy ComplInt strategy [11] [12] Simulated Annealing [4] Random Walk strategy [13] Due to its flexibility considering the interfaces to diverse simulation tools and due to the number of implemented optimization algorithms, MODOS is very well suited for the different optimization tasks arising during the development of microsystems. Fig.…”

Section: Algorithmsmentioning

confidence: 99%

Model based design optimization of micromechanical systems, based on the Cosserat theory

Wiegand

Peters

Laur

et al. 2008

2008 11th International Conference on Optimization of Electrical and Electronic Equipment

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In this paper we present the model based design optimization on a micromechanical system which is described by a VHDL-AMS behaviour model, based on the Cosserat theory. The application is a gyroscope test structure developed by STMICROELECTRONICS. The schematic of the microactuator is built up from component models developed in Lancaster within previous work. The equations of motion of the beam model were derived using Cosserat theory. The rigid body model was adjusted to capture the behaviour of the central disk with comb-drives of the microactuator target structure. Within the model based design optimization system MODOS developed at the UNIVERSITY OF BREMEN a model optimization and a model based design optimization were performed. In this report we represent the theoretical background of the modeling and optimization process and publish the received results.

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“…In nowadays design flows, the following problem arises: how to optimally design of microsystems with embedded passive modules (PM), especially, in frequency domain (Peters et al, 2002). Good reliable projects need exact, fast and robust design methods and tools (Dziedzic, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…In such situations some enhanced methods for modelling as well as for solving circuit equations have been developed including linear solvers. For enhancing the convergence of heuristic search methodologies the combinatorial optimization-approximation method has been proposed in Peters et al (2002). It should be noticed, that effectiveness of the optimization process depends also on effectiveness of AC analysis method.…”

Section: Introductionmentioning

confidence: 99%

Effective optimization of integrated circuits with embedded passive modules by applying semisymbolic LCS AC analysis method

Balik

2011

Microelectronics International

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Purpose -The purpose of this paper is to present a new method of optimization of electronic integrated circuits (IC) with imbedded passive modules (PM). The reported method constitutes an attempt to streamline the optimization process in the AC electrical model stage of the RF Microsystems design. Design/methodology/approach -In this method, the PM are described in symbolic form while the IC blocks remain described numerically. Whole PM can be represented as some sequence of expressions containing crucial model parameters, nominal and parasitic, which are first precompiled and next, merged automatically with the main program. Findings -The input data can be updated online according to the user's desiderata. Further, the system offers the possibility to optimize PM in diverse circumstances as well as using different technologies, and including parasitic effects. Originality/value -Usage of the semi-symbolic method for IC with embedded PM optimization based on large-change sensitivity AC analysis method, which appears to be a very efficient and flexible approach to solving such problems.

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<title>Enhanced optimization algorithms for the development of microsystems</title>

Cited by 3 publications

References 9 publications

An adaptive feedback circuit for MEMS resonators

An adaptive feedback circuit for MEMS resonators

Model based design optimization of micromechanical systems, based on the Cosserat theory

Effective optimization of integrated circuits with embedded passive modules by applying semisymbolic LCS AC analysis method

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