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Poster Presentations

Electroadhesive Auxetics as Programmable Layer-Jamming Surfaces for Formable Crust Shape Displays

Shape displays are a class of haptic devices that enable whole-hand haptic exploration and manipulation of 3D surfaces. Traditional shape displays are made using linear actuator arrays that move up and down to render a discretized 2.5D depth map. However, this implementation scales poorly to smooth and curved surfaces, where their mechanical complexity and cost inhibit immersive render resolutions and display sizes. We developed an auxetic layer-jamming surface actuator, using electroadhesion to programmatically lock select areas of stacked flexible printed circuit boards together.  These surface actuators (conceptually called a "formable crust" in the literature) can be overlaid as a strain limiting layer on top of an inflatable pneumatic pouch, where locally locking selected auxetic unit cells (AUCs) creates global shape change.

We first characterized our auxetic sheets, showing a maximum in-plane stiffness variation of 7.6x with a power consumption of 50 µW/AUC. We then demonstrate their ability to programmatically modulate the continuous shape output of a formable crust shape display.

[3] A. M. Rauf, Jack S. Bernardo, and S. Follmer, “Electroadhesive Auxetics as Programmable Layer Jamming Skins for Formable Crust Shape Displays”, Accepted to IEEE ICRA 2023, doi: 10.48550/arXiv.2211.05375.

Nonlinear Dynamics of MEMS Electrostatic Gap Closing Actuators

Lateral electrostatic gap closing actuators (GCAs) are widely used in MEMS (micro-electromechanical systems) for their large displacements (> 1 mm), low power draw (< 1 mW), high areal force density (>1 mN/mm2). While quasi-static models have been well characterized in the literature, dynamics models remain a challenging problem due to nonlinear electromechanical and fluidic damping forces at sub-millimeter size scales. This opens optimization potential for faster, higher-force planar actuation within MEMS. 

We developed a nonlinear dynamics model for GCAs operated in air and underwater. We factor in finger bending and the release phase’s initial velocity over prior work, and we systematically study the effect on GCA pull-in and release time by varying both the finger length and the release spring constant. Simulation results are then compared to experimental data with good conformity. We also apply this dynamics model to optimize electrostatic inchworm motors for drive frequencies up to 40 kHz and speeds up to 415 mm/s, over 11x faster than what has been previously reported.

[2] A. M. Rauf, D. S. Contreras, R. M. Shih, C. B. Schindler, and K. S. J. Pister, “Nonlinear Dynamics of Lateral Electrostatic Gap Closing Actuators for Applications in Inchworm Motors,” Journal of Microelectromechanical Systems, vol. 31, no. 1, pp. 29–36, Feb. 2022. doi: 10.1109/JMEMS.2021.3130957 (PDF copy here).

MEMS Control Surfaces for Cigar-Sized Rockets

Miniature autonomous rockets provide an interesting solution to wide-area, low-payload, and single-use distribution problems such as rapid area surveillance and unmanned air vehicle (UAV) interception, where conventional systems like drones and ballistic missiles tend to be either expensive or unportable. Our goal is to develop an aerodynamic MEMS control surface for cigar-sized rockets. This device uses electrostatic inchworm motors to rotate a thin silicon fin, serving as an airfoil to generate lift as the rocket flies. 

The MEMS control surface generates 3.6 μNm of torque about the rocket's body axis in 13.3 m/s airflow with a 5.1° angle of attack, inducing a maximum roll velocity of 100°/s. The device achieves 2x larger deflections than comparable piezoelectrics and shrinks device scales by 100x compared to prior MEMS control surfaces.

I also got trained in a Class 10 cleanroom, the Marvell Nanofabrication Laboratory, on how to fabricate these MEMS devices! Below is a picture of me, fully gowned-up on my first day in the cleanroom.

[1] A. M. Rauf, B. G. Kilberg, C. B. Schindler, S. A. Park, and K. S. J. Pister, “Towards Aerodynamic Control of Miniature Rockets with MEMS Control Surfaces,” in IEEE MEMS 2020, Jan. 2020, pp. 523–526. doi: 10.1109/MEMS46641.2020.9056431 (PDF copy here).

Two-Axis Helmholtz Coils for Spintronics Testing

Spintronics, or the study of an electron's intrinsic spin and associated magnetic moment, enables fast and low-power electromagnetic sensors like the magnetic field sensors in hard disk drive heads and spin-transfer torque magnetic random-access memory (STT-MRAM). Testing these devices in the lab requires precise calibration of an applied magnetic field's magnitude and direction, but traditional two-axis Helmholtz coils or projected field magnets/rotary tables need to be very large to avoid fringing or suffer from interference with high-frequency device signal lines.

We developed a custom two-axis Helmholtz coil to generate magnetic fields up to 150 mT with controllable XY orientation (pictured below, on the left). By amplifying magnetic fields through a vim var steel core (µr >10k), moving signal probes up away from the test area, and optimizing the head geometry, we achieved near-negligible fringing and field uniformity in a small form factor. Field strength was controlled via a PID controller in LabVIEW.

One application of this testing setup were coplanar waveguides (CPWs) for spin pumping, which uses a resonant ferromagnet as a source of spin current to measure the spin transport properties of ferromagnet/metal interfaces. Traditional flip chip testing setups for CPWs struggle with RF reflections, since the setup cannot be calibrated to match resistances through soldered connectors. We hypothesized that our microwave probe station could enable faster, lower-loss coupling for lithographically patterned Au/Py/Si spin pumping (pictured below, on the right). We found that our setup accurately measured the CPW's RF transmission, but fabrication defects caused poor signal-to-noise ratios in the output ferromagnetic resonance.

These projects were performed in Professor Sayeef Salahuddin's lab, with mentorship from Samuel Holladay and Niklas Roschewsky.