Transdermal biosensors use arrays of microneedle electrodes to monitor molecular biomarkers in real time. While recent advances have produced soft, stretchable electrodes that conform to dynamic tissues—improving signal quality and biocompatibility—their fabrication remains challenging due to flexible substrates’ susceptibility to distortion and the need for precise microneedle placement. To address this issue, we present a novel microgripper module for automated microneedle placement onto flexible substrates during wearable biosensor fabrication. This system improves precision and scalability, while reducing the variability and time associated with manual microneedle placement. To achieve this, our microgripper evolved through several iterations, culminating in a modular design with detachable walls, a homing microswitch, a fixed front wall with an integrated gripper arm, and a matching gripper arm attachment that fastens to a linear stepper motor. The encapsulating frame guides the motor and gripper assembly, preventing any unwanted rotations, while the reinforced structure—with extended fastening points and flush mounting holes—supports slim gripper arms capable of accurately manipulating microneedles spaced at 4 mm intervals. Calibration using G-code and a precision digital caliper substantially improved accuracy, reducing displacement errors from ~80% pre-calibration to below 0.1% post-calibration. Homing tests, in which the gripper repeatedly returned to its microswitch-defined zero position, produced a consistent open-gripper-arm distance of 13.203 mm, while functional tests demonstrated successful, repeatable microneedle grasping. These results validate the microgripper’s precision and repeatability, enabling scalable, automated fabrication of microneedle electrode biosensors on flexible substrates.

  Through-Wafer Vias (TWVs) are a solution for enabling high-density interconnects in wafer scale systems and advanced packaging, yet integrating them is limited due to challenges in understanding the mechanical stress they impose. In the Silicon-Interconnect Fabric (Si-IF) — a silicon based integration platform with fine pitch wiring layers — TWVs have a role in enabling vertical connection and routing signals or power through the substrate. They also enable double-sided substrates, which allow for backside power delivery to the active dies through the vias. This study investigates the localized stress fields and keep-out zones generated by TWV geometry due to thermal annealing, using simulation with ANSYS Mechanical Workbench and Electronics Desktop (Q3D). Parametric sweeps are conducted with diameters ranging from 50–150 µm and thicknesses of 500–700 µm are modeled to evaluate stress after copper fill and annealing, as well as their per-unit-length RLGC parameters. These are done for single and dual-via configurations to quantify Keep-Out Zones (KOZ) based on stress field overlap. Results indicate that larger diameters/thicknesses significantly increase localized stress. Simulations show that the pitch value of 200 microns has too much stress overlap, while 500 microns has no overlapping, making intermediate values ideal. From KOZ measurements, safe via spacing is estimated to be ~1.5–2× the via diameter. These findings will provide a foundation for design rules for safe via density and spacing without doing full-wafer meshing. This work contributes to the broader effort of enabling scalable 3D interconnects, which will pave the way for more efficiency within advanced technology.  

The upstream shear layer of a jet in crossflow can transition from convective to absolute/global instability as the jet-to-crossflow momentum flux ratio J decreases. Convectively unstable flows involve relatively weak disturbances, but absolutely unstable flows involve strong disturbances and thus are more difficult to control.  Understanding this transition is essential for predicting and controlling jet penetration and mixing, key objectives in many engineering applications. Prior work proposed an analogy between the upstream shear layer and a counter-current shear layer, known to become absolutely unstable above a critical J value. This project experimentally investigates and identifies dominant instability modes across a range of jet conditions. As part of this summer's objectives, we used hot wire anemometry to measure velocity fluctuations and extract dominant frequency content in the upstream shear layer. I contributed by performing hot-wire data acquisition, identifying spectral trends, and developing MATLAB tools to track and address missing data segments. We also improved experimental efficiency by designing and fabricating custom components for the experiment using CAD, and conducted calibration and troubleshooting for both hot-wire probes and laser sheet alignment and set up. Preliminary results reveal frequency trends that support the presence of absolute instability at specific J values for different jet-to-crossflow density ratios S This work contributes to a better understanding of how instability mechanisms influence jet penetration and mixing, ultimately aiding in the prediction and control of transverse jet behavior.   

Radio-Frequency Power Amplifiers (RFPAs) are a vital part of RF communications but due to their nonlinear characteristics, they can introduce significant distortion in transmitted signals. Use of RFPAs in present-day operations is done mostly in the linear region, or the input voltage range for which the RFPA output is approximately undistorted and purely amplified, as required by federal regulations. However, utilizing more of the RFPA output range is likely a more energy and cost-efficient solution for RF communications. We aim to expand this linear region by using Digital Predistortion (DPD) with Deep Learning to linearize the response of RFPAs and use as much of the output range as possible without introducing significant distortion. We trained a model of an RFPA from a publicly available dataset on a 200 MHz RFPA, and used that model to train an inverse RFPA model, and filtered and multiplied at the input for the necessary saturation and amplification behavior. This system was cascaded with the inverse model to create our DPD system, whose output was fed as the input of our RFPA model. We tested with sinusoidal signals with random noise at the input and recorded the output signals, and did the same for just the RFPA model without any DPD operations. We found that amplification without DPD had a Mean Squared Error (MSE) of 4.4425 x 10^-3 from the desired signal, while amplification with DPD had an MSE of 3.1658 x 10^-5, showing that DPD can reduce signal distortion by 2 orders of magnitude.

The Terahertz (THz) frequency band offers unique advantages for high-resolution sensing and imaging due to its non-ionizing nature and sensitivity to molecular composition. This research presents a versatile free-space THz system built from a core set of components, demonstrating its capability in two distinct configurations: high-precision non-contact displacement measurement and material characterization of aqueous solutions. Based on a 400 GHz transmitter and receiver, the system was used in two configurations. For vibrometry, it was arranged as a Michelson interferometer, where a split THz beam’s interference pattern measures target displacement. To determine the system’s performance, a simulation model was calibrated using initial experimental data. For material characterization, the setup was reconfigured into a focused-beam reflectometer by removing the reference arm. The beam was focused directly onto a sample, and the reflected power was measured across a range of frequencies to analyze its properties. The interferometric configuration proved highly effective for signal reconstruction. The calibrated simulation showed a displacement resolution of 1 μm or better, limited primarily by the receiver’s noise floor. This model also successfully simulated the reconstruction of periodic waveforms, such as pure tones and human heartbeat patterns. In the reflectometer configuration, the system successfully differentiated between several samples, including water, oil, saline, and glucose solutions. These materials exhibited unique, frequency-dependent reflective signatures across the 370-430 GHz band, enabling their identification. Furthermore, the system demonstrated sensitivity to dissolved solutes, clearly distinguishing a 1 M glucose solution from pure water based on the reflected power, confirming its potential for concentration sensing.