Physical Sensors & Devices

Research that includes:

  • Silicon MEMS actuators: comb, electro-thermal, and plastic deformation
  • Precision electronic sensing and measurements of capacitive, frequency, and coulombic MEMS variables
  • Structures and architectures for gyroscopes, accelerometers, micro strain gauges for direct application to rigid structures e.g., steel, and levitated MEMS

BPN467: Aluminum Nitride Ultrasonic Doppler Velocity Sensor

Stefon E. Shelton
Hongsoo Choi

The goal of this project is to develop a high precision MEMS ultrasonic Doppler velocity sensor utilizing an array of Aluminum Nitride transducer elements for use in personal navigation units. Aluminum Nitride has been chosen for its desirable piezoelectric properties and compatibility with CMOS processes which allows for on chip integration of MEMS and electronics. In our device we aim to produce an ultrasound source-receiver pair with integrated signal processing circuitry on a single chip. To determine the velocity we will be developing and implementing an efficient and accurate...

BPN962: Insect-Scale Flying Robots

Fanping Sui
Kamyar Behrouzi
Wei Yue

Insect-scale untethered flight with maneuverability is very challenging toward possible practical applications and attitude-stabilized flight (hovering) is one of the first steps for long-time air flight operations. In this project, we introduce the insect-scale, untethered, rotating-wing aerial vehicles with inherent stability by the gyroscopic effect to achieve several key advancements: (1) powered by alternating magnetic fields wirelessly; (2) 160-mg in weight and 20.0-mm-in-diameter in size – smallest untethered flying robot in the world; and (3) attitude-stabilized flights (...

BPN974: Lidar-Camera Fusion for Autonomous Driving

Philip L. Jacobson

Within the past few decades, the goal of fully-autonomous vehicles has moved from a thought experiment to a potential reality thanks to advances in machine intelligence. One of the key challenges to still be overcome is the building of robotic perception systems which can achieve performance on-par with or surpassing that of humans. Currently, most autonomous driving researchers rely on several different modalities for collecting visual information, namely lidar, radar, and cameras. Although relying on lidar for perception has the drawback of high cost, maturing lidar technology has opened...

BPN876: Metal-Organic Frameworks for Chemical Sensing with High Selectivity

Alireza Pourghaderi
Isaac Zakaria

A classic challenge in gas sensing is the tunability of the sensing material for the selective absorption of target gases without interference from unwanted species. Metal-organic frameworks (MOFs), made up of metal-cluster nodes connected by organic linkers, can achieve selective adsorption owing to their high chemical and structural tunability. Their selectivity and flexibility make MOFs attractive for gas sensing, as realized in novel low-power, low-footprint, on-chip devices such as the chemical-sensitive field-effect transistor, previously demonstrated by our group. In this...

BPN913: Mixed-Dye ZIF-8-Based Colorimetric Carbon Dioxide Sensing for Robust Indoor Air Quality Monitoring

Adrian K. Davey

Indoor levels of carbon dioxide (700 parts per million and up), when coupled with volatile organic compounds (VOCs) under most temperature and humidity environments, can induce fatigue, nausea, nasal irritation, and related human health symptoms. Toward the realization of rapid, inexpensive, passive, and visually-obvious indoor gas sensors, we present dye-functionalized metal-organic frameworks (MOFs), which employ distinct color changes to measure indoor carbon dioxide concentrations. Our latest generation of the sensor, based on the coupling of multiple dyes blended with MOF...

BPN948: Wireless Tactile Stimulation with MEMS Inchworm Motors

Dillon Acker-James

The goal of this project is to make an untethered MEMS tactile stimulator. Electrostatic inchworm motors made in SOI substrates routinely generate 1-15 mN of force and 2 mm/s travel, making them a viable option for a millimeter-scale wireless tactile stimulator. Collaborating with Professor Eric Paulos and his students, our first step is to conduct haptic sensation surveys in order to understand what a user feels based on different forces. Our current chips provide a force range of 1mN up to 15mN, but we plan to increase this in the future. Our next step would be to integrate the MEMS...

Facile Fabrication of Multilayer Stretchable Electronics via a Two-Mode Mechanical Cutting Process

Renxiao Xu
Peisheng He
Guangchen Lan
Kamyar Behrouzi
Yande Peng
Dongkai Wang
Tao Jiang
Ashley Lee
Yu Long
Liwei Lin
A time- and cost-effective fabrication methodology via a two-mode mechanical cutting process for multilayer stretchable electronics has been developed without using the conventional photolithography-based processes. A commercially available vinyl cutter is used for defining complex patterns on designated material layers by adjusting the applied force and the depth of the cutting blade. Two distinct modes of mechanical cutting can be achieved and employed to establish the basic fabrication procedures for common features in stretchable electronics, such as the metal interconnects, contact...

Programmable Tactile Feedback Patterns for Cognitive Assistance by Flexible Electret Actuators

Tao Jiang
Wenying Qiu
Zhaoyang Li
Xing Ye
Yuhan Liu
Yushi Li
Xiaohao Wang
Junwen Zhong
Xiang Qian
Liwei Lin

Advanced tactile feedback systems are important tools in the field of human–machine interfaces. In this work, an airflow-assisted corona charging process is utilized to charge films made of electret material for the construction of a sandwich-structured flexible actuator system. With a voltage as low as 20 V, this flexible actuator can stimulate skin sensations for basic tactile feedback functions. Under a driving voltage of 200 V, the system can generate an output force of ≈55 mN, which is larger than that of the output force by cellphones under the vibration mode. Utilizing these...

Deep Learning for Non-Parameterized MEMS Structural Design

Ruiqi Guo
Fanping Sui
Wei Yue
Sedat Pala
Kunying Li
Renxiao Xu
Liwei Lin

The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances. However, it is challenging for researchers to rationally consider a large number of possible designs, as it would be very time- and resource-consuming to study all these cases using numerical simulation. In this paper, we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features. Design candidates are represented in a...

Jordan L. Edmunds

Electrical Engineering and Computer Sciences
Professor Michel M. Maharbiz (Advisor)
Ph.D. 2022