Evolutionary design optimization of MEMS: a review of its history and state-of-the-art

Wang, Pan; Lu, Qibing; Fan, Zhun

doi:10.1007/s10586-018-2085-3

Cited by 12 publications

(3 citation statements)

References 35 publications

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“…Genetic algorithms (GAs) have been used to find optimal MEMS designs by several research groups [1]. GAs are used because of their ability to evolve the design of MEMS towards multi-objective performance needs.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Micro-Electromechanical Lorentz Actuator Using a Surrogate Model Accelerated Genetic Algorithm

Buhr,

Shafai,

Park

et al. 2024

Eurosensors 2023

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Section: Introductionmentioning

confidence: 99%

Optimization of Micro-Electromechanical Lorentz Actuator Using a Surrogate Model Accelerated Genetic Algorithm

Buhr,

Shafai,

Park

et al. 2024

Eurosensors 2023

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“…An innovative capacitive moisture sensor was designed for polymers to demonstrate the relationship between the sensor's geometrical parameters and performance [18]. Despite the limitations associated with some commonly used microelectromechanical systems (MEMS) design methods, evolutionary computations have recently become famous for designing and optimizing MEMS [19].…”

Section: Introductionmentioning

confidence: 99%

Design Optimization of a Capacitive Sensor for Mass Measurement of Nanometer-Sized Exhaust Carbon Particles

Kulkarni

Chorage

2023

Proc. eng. technol. innov.

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Nanometer-sized carbon particulates generated by incomplete combustion in heavy-duty vehicles are harmful to human health. A high-resolution technique is needed to detect and measure these pollutants. This study aims to optimize a capacitive sensor design for detecting and measuring particulates. Firstly, the effect of design parameters on particulate detection and sensor compliance sensitivity is investigated by using the finite element method. By comparing the simulation results with literature findings for performance validation, the sensor structure is optimized to detect lower particulate concentrations. The simulation result shows that particulate detection sensitivity has linear variations with changes in particulate mass. With optimum electrode spacing and top insulation layer thickness of 5 µm, the sensor can detect a particulate deposition of 0.033 mg/min and generate a maximum capacitance of 581 pF. Since the optimized design can measure particulate deposition at a lower range and with higher sensitivity, it is suitable to be applied to detect nanometer-sized carbon particulates.

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“…Micro-electro-mechanical systems (MEMS) comprise both mechanical and electronic components manufactured on a common silicon substrate, and have gained significant attention in the last three decades. Due to the miniaturized size, low cost, high manufacturing throughput, and expectance of superior performance, MEMS are used in a wide range of consumer, automotive, medical, and industrial products [1,2]. With the increase in the integration density and the reduction in the feature size, high-aspect-ratio (HAR) structures in silicon play an increasingly important role in micro-sensors and actuators [3,4].…”

Section: Introductionmentioning

confidence: 99%

MEMS High Aspect Ratio Trench Three-Dimensional Measurement Using Through-Focus Scanning Optical Microscopy and Deep Learning Method

Shi

Gao

et al. 2022

Applied Sciences

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High-aspect-ratio structures have become increasingly important in MEMS devices. In situ, real-time critical dimension and depth measurement for high-aspect-ratio structures is critical for optimizing the deep etching process. Through-focus scanning optical microscopy (TSOM) is a high-throughput and inexpensive optical measurement method for critical dimension and depth measurement. Thus far, TSOM has only been used to measure targets with dimension of 1 μm or less, which is far from sufficient for MEMS. Deep learning is a powerful tool that improves the TSOM performance by taking advantage of additional intensity information. In this work, we propose a convolutional neural network model-based TSOM method for measuring individual high-aspect-ratio trenches on silicon with width up to 30 μm and depth up to 440 μm. Experimental demonstrations are conducted and the results show that the proposed method is suitable for measuring the width and depth of high-aspect-ratio trenches with a standard deviation and error of approximately a hundred nanometers or less. The proposed method can be applied to the semiconductor field.

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Evolutionary design optimization of MEMS: a review of its history and state-of-the-art

Cited by 12 publications

References 35 publications

Optimization of Micro-Electromechanical Lorentz Actuator Using a Surrogate Model Accelerated Genetic Algorithm

Optimization of Micro-Electromechanical Lorentz Actuator Using a Surrogate Model Accelerated Genetic Algorithm

Design Optimization of a Capacitive Sensor for Mass Measurement of Nanometer-Sized Exhaust Carbon Particles

MEMS High Aspect Ratio Trench Three-Dimensional Measurement Using Through-Focus Scanning Optical Microscopy and Deep Learning Method

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