Yield analysis via induction of process statistics into the design of MEMS and other microsystems

Vudathu, S.P.; Duganapalli, K.K.; Laur, R.; Kubalinska, D.; Bunse-Gerstner, Angelika

doi:10.1007/s00542-006-0263-3

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…Using tweezers might help to handle the problem of a defined grip process, but the grip forces still cannot be controlled. Grip forces from humans can easily exceed the maximum force that microparts can withstand (Vudathu, Duganapalli, Laur, Kubalinska, & Bunse-Gerstner, 2007). Consequently, the risk of damaging fragile microparts during the gripping process is very high.…”

Section: Challenges In Microassemblymentioning

confidence: 99%

Telepresence in Industrial Applications: Implementation Issues for Assembly Tasks

Radi

Nitsch

2010

Presence: Teleoperators and Virtual Environments

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In contrast to automated production, human intelligence is deemed necessary for successful execution of assembly tasks that are difficult or expensive to automate in small and medium lots. However, human ability is hindered in some cases by physical barriers such as miniaturization or in contrast, very heavy components. Telepresence technology can be considered a solution for performing a wide variety of assembly tasks where human intelligence and haptic sense are needed. This work highlights several issues involved in deploying industrial telepresence systems to manipulate and assemble microparts as well as heavy objects. Two sets of experiments are conducted to investigate telepresence related aspects in an industrial setting. The first experiment evaluates the usefulness of haptic feedback for a human operator in a standard pick-and-place task. Three operation modes were considered: visual feedback, force feedback, and force assistance (realized as vibration). In the second experiment, two different guidance strategies for the teleoperator were tested. The comparison between a position and a velocity scheme in terms of task completion time and subjective preferences is presented.

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Section: Challenges In Microassemblymentioning

confidence: 99%

Telepresence in Industrial Applications: Implementation Issues for Assembly Tasks

Radi

Nitsch

2010

Presence: Teleoperators and Virtual Environments

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“…Studies on MEMS devices have pointed out that manufacturing process variations have a close relationship with performance drift and device failure [ 22 , 25 , 31 ]. As the basic element in MEMS, the resonant frequency of the doubly-clamped beam underlies the majority of engineering designs.…”

Section: Problem Solutionmentioning

confidence: 99%

“…In addition, magnetometers adopted multiphysics-based optimization and nonlinear situations [ 14 ], and gyroscopes focused on the packaging with double yield [ 15 ]. Except for unique device analysis, methods that are generally applicable have advanced the processing improvement further [ 3 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Mirzazaden et al [ 16 , 17 , 18 ] investigated morphology uncertainties with reduced-order models through on-chip tests.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid the brute-force Monte Carlo (MC) scheme, Pfingsten et al [ 21 ] considered a Bayesian Monte Carlo approach for yield estimation, with 90.0% computational savings but the same accuracy, compared with MC schemes. Vudathu et al [ 22 , 26 ] applied a sensitivity analyzer for MEMS (SAM) by worst-case analysis, revealing the effects of parametric variations on performance and yield. Achievements have been reached to get a balance between precision and calculation—for example, the Sigma-Point approach applied to MEMS resonators with four orders of magnitude faster than MC [ 23 ], the generalized polynomial chaos (GPC) framework to handle stochastic coupled electromechanical analysis with the same precision and one order of magnitude faster compared with MC [ 24 ], and the Taguchi parameter design and statistical process-control method to minimize variability in performance response to fluctuations [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Modeling of the Effect of Process Variations on a Micromachined Doubly-Clamped Beam

Gao¹,

Zhou²,

Huang³

2017

Micromachines

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In the fabrication of micro-electro-mechanical systems (MEMS) devices, manufacturing process variations are usually involved. For these devices sensitive to process variations such as doubly-clamped beams, mismatches between designs and final products will exist. As a result, it underlies yield problems and will be determined by design parameter ranges and distribution functions. Topographical changes constitute process variations, such as inclination, over-etching, and undulating sidewalls in the Bosch process. In this paper, analytical models are first developed for MEMS doubly-clamped beams, concerning the mentioned geometrical variations. Then, finite-element (FE) analysis is performed to provide a guidance for model verifications. It is found that results predicted by the models agree with those of FE analysis. Assigning process variations, predictions for performance as well as yield can be made directly from the analytical models, by means of probabilistic analysis. In this paper, the footing effect is found to have a more profound effect on the resonant frequency of doubly-clamped beams during the Bosch process. As the confining process has a variation of 10.0%, the yield will have a reduction of 77.3% consequently. Under these circumstances, the prediction approaches can be utilized to guide the further MEMS device designs.

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“…All these limit the industrialization of most MEMS devices. Actual device performance variation is always dependent on the geometric variations [4], and MEMS devices benefit especially from the smaller size. Attempts to evaluate the system statistically and analytically have been proved to be valid [5,6].…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Yield Enhancement of MEMS Devices by Considering Multi-Process Variations

Gao¹,

Li²,

Huang³

2015

KEM

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A methodology is developed statistically to make MEMS devices robust to process variations to improve manufacturability and yield. Two approaches are applied to discuss the effects of multi-process variations. Comparisons have been made between the proposed method and Monte Carlo simulations, which confirm the robustness of the proposed one with performance error less than 4%. Experiments on beams and comb-drive resonator verified the effectiveness of the methodology and it is useful for practical device designs to be more robust to process variations and yield enhancement realization.

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Yield analysis via induction of process statistics into the design of MEMS and other microsystems

Cited by 6 publications

References 7 publications

Telepresence in Industrial Applications: Implementation Issues for Assembly Tasks

Telepresence in Industrial Applications: Implementation Issues for Assembly Tasks

Modeling of the Effect of Process Variations on a Micromachined Doubly-Clamped Beam

Statistical Analysis and Yield Enhancement of MEMS Devices by Considering Multi-Process Variations

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