Ann Arbor, MI · Looking for hardware internships

Jaden Rhee

Electrical Engineering Student · Hardware & PCB Design

I design PCBs and the hardware around them, and write the firmware they run. Currently designing flex-PCB IMU wearables in a biomechanics lab at Michigan.

View projects Résumé (PDF)

Projects

The View in 3D models come straight from the real KiCad and CAD files.

Isometric render of the JRWatch 4-layer smartwatch PCB

PCBLow-powerZephyrBLE

JRWatch, a Low-Power BLE Smartwatch

A 36 mm nRF52840 smartwatch board that sleeps at about 15 µA, designed entirely in code.

Problem: Most smartwatches last a few days on a charge because low power is an afterthought. I wanted a watch that runs for months on a 150 mAh cell, with every current number justified before ordering the board.
What I built: A 4-layer 36 x 36 mm board with an nRF52840, an nPM1300 PMIC with two switched power domains and a 370 nA ship mode, a BMI270 IMU for motion wake, and a Sharp memory-in-pixel display that holds a static watch face at about 4 µA. The schematic is written in SKiDL, placement and routing are scripted through the pcbnew API, and the Zephyr firmware builds in CI with a custom board definition. The OpenSCAD case is dimensioned from the board file.
Outcome: ERC and DRC are clean. The firmware fits in 231 KiB of flash and 40 KiB of RAM. The power budget works out to 4 to 8 months per charge, with each line cited in the verification report. Next step is confirming the numbers with a PPK2 at bring-up.

nRF52840 · Zephyr RTOS · SKiDL · KiCad · nPM1300 · BMI270 · C · OpenSCAD

GitHub

Top render of the WiggleCam RP2040 co-processor PCB

PCBRP2040ImagingFirmware

WiggleCam, a 4-Lens Wigglegram Camera

A handheld camera with four synchronized 16 MP lenses, built around an RP2040 co-processor board that drives the flash, shutter, and battery telemetry.

Problem: Film wigglegram cameras need scanning and manual frame alignment, and multi-camera rigs ghost on moving subjects because the sensors fire at slightly different times. A Raspberry Pi 5 is also not suited to hard real-time or analog work: the LED flash needs constant-current drive with hardware safety limits, the shutter needs reliable debounce, and the battery needs monitoring, none of which should steal cycles from image processing.
What I built: A 76 x 50 mm 4-layer RP2040 co-processor board sits between the Pi and the hardware over a 2x6 header mapped onto Pi 5 GPIO, handling constant-current LED flash, shutter debounce, battery telemetry, and multi-camera synchronization over I²C and UART. Its schematic is SKiDL, placement is scripted, low-speed nets go through Freerouting with a DRC gate at JLCPCB limits at every stage, and the Pico SDK firmware exposes an I²C register file with a UART fallback. Four 16 MP sensors sit 40 mm apart behind the faceplate and are merged through a single CSI interface by an Arducam Camarray HAT, so all four exposures happen at the same instant. Python on the Pi 5 splits the frame, aligns the views with phase correlation, and renders a looping gif served over the camera's own Wi-Fi hotspot as a QR code. The body is parametric OpenSCAD with a BMS-protected 4x18650 supply.
Outcome: The co-processor board is fully routed with zero DRC violations and no unconnected nets at JLCPCB 4-layer rules, with 24 measured verification checks and fab files ready to order. The camera software is done and the physical build is in progress, at about $550 of stock-checked parts and an estimated 4.4 hours of runtime.

RP2040 · Raspberry Pi 5 · SKiDL · KiCad · Pico SDK (C) · Arducam Camarray · Python · OpenSCAD

GitHub

Experience

Aug 2026 to Present
Research Assistant · Musculoskeletal Biomechanics & Imaging Laboratory, U-M
Ann Arbor, MI
- Designed the electronics and flex-PCB layout in Altium for neck- and back-mounted IMU wearables, translating sensing requirements into schematics, circuit design, component placement, and routed boards.
- Fabricated and brought up sensor prototypes by soldering and reworking flex-PCBs, then troubleshooting electrical faults and IMU signals with oscilloscopes, DMMs, bench supplies, and lab instrumentation.
- Built data-acquisition code and ML-based analysis workflows for multi-axis IMU measurements, connecting hardware prototypes through data collection, evaluation, and validation of wearable biomechanics sensing.
Apr 2026 to Aug 2026
ECU Design and Validation Intern · Bosch
Plymouth, MI
- Analyzed Tesla Model 3 ECU wake-up waveforms as a known-good baseline for an intermittent startup failure, identifying abnormal power-rail timing and incomplete discharge in the failure condition.
- Designed a high-speed ECU power-switching fixture with PMOS and NPN transistors to reproduce abnormal rapid power-up (83 to 85 µs) across 5 ECUs while preserving ground reference; measured startup current above the 30 A probe range.
- Instrumented ECU PCBs for bring-up and debug, tracing the failure from vehicle input into internal power rails and correlating rail collapse with ground-reference movement using PicoScope and Teledyne LeCroy oscilloscopes.
- Simulated buck and boost power circuits in CST and PSpice; recreated a boost-converter schematic and layout in KiCad and bench-tested automotive regulator efficiency against datasheet expectations.
Sep 2025 to May 2026
Research Assistant · Human-Automation Technology Lab, MSU
East Lansing, MI
- Developed a glasses-mounted wearable IMU prototype on Arduino Nano 33 BLE for head-nod detection, integrating motion sensing and data acquisition to capture 1 to 2 s multi-axis inertial windows for algorithm development and validation.
- Evaluated DSP and deep-learning speaker-verification pipelines for resource-constrained edge hardware, optimizing MFCC, RMS, and ZCR feature extraction by about 50% and profiling accuracy, compute, and implementation tradeoffs.
- Co-authored an IEEE MWSCAS 2026 paper; developed the embedded BLE case study and contributed experimental analysis, figures, and tables comparing deployable speaker-verification approaches.
Education
University of Michigan
B.S. Electrical & Electronics Engineering, Minor in Computer Science
Ann Arbor, MI · Expected 2028
Michigan State University, Honors College
B.S. Electrical Engineering (transferred)
East Lansing, MI · GPA 3.7 · 2025 to 2026

Research

AcceptedIEEE MWSCAS 2026

Evaluating Edge Speaker Verification ML Pipelines: A Multi-Variable Optimization Framework

Nitish Maindoliya, Chenxin Zhang, Jaden Rhee, and Andrew J. Mason

We profiled 225 combinations of audio features and classifiers for real-time speaker verification on microcontrollers with 512 KB of memory. Classic DSP features feeding an RBF-kernel SVM landed on the best tradeoff of accuracy, latency, and memory, where deep learning embeddings were too heavy to fit.

Read the paper (PDF)