Keyword search (4,164 papers available)

"optimization" Keyword-tagged Publications:

Title Authors PubMed ID
1 Tuning Deep Learning for Predicting Aluminum Prices Under Different Sampling: Bayesian Optimization Versus Random Search Alicia Estefania Antonio Figueroa 41751647
CONCORDIA
2 Optimizing Mixtures of Metal-Organic Frameworks for Robust and Bespoke Passive Atmospheric Water Harvesting Harriman C; Ke Q; Vlugt TJH; Howarth AJ; Simon CM; 41427123
CHEMBIOCHEM
3 A Deep Learning-Based Ensemble System for Brent and WTI Crude Oil Price Analysis and Prediction Zhang Y; Lahmiri S; 41294965
JMSB
4 Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm Lahmiri S; Tadj C; Gargour C; 41294952
ENCS
5 Robust and Compact Electrostatic Comb Drive Arrays for High-Performance Monolithic Silicon Photonics Fasihanifard M; Packirisamy M; 41156349
ENCS
6 Cooperative Schemes for Joint Latency and Energy Consumption Minimization in UAV-MEC Networks Cheng M; He S; Pan Y; Lin M; Zhu WP; 40942666
ENCS
7 Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites Ramezani G; Silva IO; Stiharu I; Ven TGMV; Nerguizian V; 40283268
ENCS
8 What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA Daniel A; Savary P; Foltête JC; Vuidel G; Faivre B; Garnier S; Khimoun A; 39417711
BIOLOGY
9 Topology optimization of adaptive sandwich plates with magnetorheological core layer for improved vibration attenuation Zare M; Sedaghati R; 39398530
ENCS
10 Discovery and preclinical development of a therapeutically active nanobody-based chimeric antigen receptor targeting human CD22 McComb S; Arbabi-Ghahroudi M; Hay KA; Keller BA; Faulkes S; Rutherford M; Nguyen T; Shepherd A; Wu C; Marcil A; Aubry A; Hussack G; Pinto DM; Ryan S; Raphael S; van Faassen H; Zafer A; Zhu Q; Maclean S; Chattopadhyay A; Gurnani K; Gilbert R; Gadoury C; Iqbal U; Fatehi D; Jezierski A; Huang J; Pon RA; Sigrist M; Holt RA; Nelson BH; Atkins H; Kekre N; Yung E; Webb J; Nielsen JS; Weeratna RD; 38596311
BIOLOGY
11 Design Optimization of a Hybrid-Driven Soft Surgical Robot with Biomimetic Constraints Roshanfar M; Dargahi J; Hooshiar A; 38275456
ENCS
12 Alternating direction method of multipliers for displacement estimation in ultrasound strain elastography Md Ashikuzzaman 38159299
ENCS
13 Lactate's behavioral switch in the brain: An in-silico model Soltanzadeh M; Blanchard S; Soucy JP; Benali H; 37865309
PERFORM
14 Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration Ge B; Najar F; Bouguila N; 37754943
ENCS
15 Design optimization and experimental evaluation of a large capacity magnetorheological damper with annular and radial fluid gaps Abdalaziz M; Sedaghati R; Vatandoost H; 37521729
ENCS
16 Designing a multi-objective closed-loop supply chain: a two-stage stochastic programming, method applied to the garment industry in Montréal, Canada Shafiee Roudbari E; Fatemi Ghomi SMT; Eicker U; 36747987
ENCS
17 Optimizing Biodegradable Starch-Based Composite Films Formulation for Wound-Dressing Applications Delavari MM; Ocampo I; Stiharu I; 36557445
ENCS
18 A flexible robust model for blood supply chain network design problem Khalilpourazari S; Hashemi Doulabi H; 35474752
ENCS
19 A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary Wan TTH; Matthews S; Luh H; Zeng Y; Wang Z; Yang L; 35372638
ENCS
20 A multiobjective model for the green capacitated location-routing problem considering drivers' satisfaction and time window with uncertain demand Alamatsaz K; Ahmadi A; Mirzapour Al-E-Hashem SMJ; 34415526
ENCS
21 Optimization of the Electrospun Niobium-Tungsten Oxide Nanofibers Diameter Using Response Surface Methodology Fatile BO; Pugh M; Medraj M; 34201513
ENCS
22 A robust optimization model for tactical capacity planning in an outpatient setting Aslani N; Kuzgunkaya O; Vidyarthi N; Terekhov D; 33215335
ENCS
23 Multidisciplinary Design Optimization of a Novel Sandwich Beam-Based Adaptive Tuned Vibration Absorber Featuring Magnetorheological Elastomer. Asadi Khanouki M, Sedaghati R, Hemmatian M 32422988
ENCS
24 Computer-Aided Diagnosis System of Alzheimer's Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model. Lazli L, Boukadoum M, Ait Mohamed O 31652635
ENCS
25 Mining Enzyme Diversity of Transcriptome Libraries through DNA Synthesis for Benzylisoquinoline Alkaloid Pathway Optimization in Yeast. Narcross L, Bourgeois L, Fossati E, Burton E, Martin VJ 27442619
BIOLOGY
26 Optimal positioning of optodes on the scalp for personalized functional near-infrared spectroscopy investigations. Machado A, Cai Z, Pellegrino G, Marcotte O, Vincent T, Lina JM, Kobayashi E, Grova C 30107210
PERFORM

 

Title:Robust and Compact Electrostatic Comb Drive Arrays for High-Performance Monolithic Silicon Photonics
Authors:Fasihanifard MPackirisamy M
Link:https://pubmed.ncbi.nlm.nih.gov/41156349/
DOI:10.3390/mi16101102
Publication:Micromachines
Keywords:MEMS actuatorselectrostatic comb driveforce intensity optimizationsilicon photonicsslab waveguide actuation
PMID:41156349 Category: Date Added:2025-10-29
Dept Affiliation: ENCS
1 Optical-Bio Microsystems Laboratory, Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, 1515 St. Catherine W., Montreal, QC H3G 2W1, Canada.

Description:

Actuating monolithic photonic components (particularly slab waveguides) requires higher force due to their inherent stiffness. However, two primary constraints must be addressed: actuator footprint and fabrication limits. Increasing the number of fingers to provide the required force is not a viable solution due to space constraints, and we must also adhere to the process design kits of standard fabrications and respect their design limits. Therefore, it is crucial to increase the actuator force output without significantly enlarging the actuator footprint while maintaining the necessary travel range. In order to achieve this, we utilize arrays of electrostatic comb drives, with each repeating cell geometry optimized to produce the highest force per actuator footprint. Our optimization strategy focuses on finger geometry, the arrangement of fingers and arms design in the comb structure, including the number of fingers per arm and arm length, ensuring that each repeating cell delivers maximum force per unit area or force intensity. Co-optimizing a repeatable, footprint-optimized comb-array unit cell (arm length, arm width, finger pitch, finger count) and validating it against an asymmetric slab waveguide load, we reach a maximum pre-pull-in force intensity of about 342 N m-2 at 70 V with about 6 µm travel, confirmed by analytical modeling, numerical simulation, and measurement. Despite fabrication challenges such as over-etching and variations in electrode dimensions, detailed SEM analyses and correction functions ensure that the theoretical models closely match the experimental data, confirming the robustness and accuracy of the design. These optimized actuators, capable of achieving substantial force output without sacrificing travel range or mechanical stability, are particularly effective for applications in optical beam steering for in-plane silicon-photonics and related optical microsystems applications.





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