ESP32S3_DEV_Minimal_LowPower
This project implements a minimal low-power development board based on the ESP32-S3.
The design focuses on reducing component count, board complexity, and idle power consumption, while still exposing the full capabilities of the ESP32-S3 for advanced development.
Unlike traditional development boards, this design does not include a USB-to-UART bridge. Instead, it leverages the native USB interface of the ESP32-S3 for programming and communication, resulting in a simpler and more efficient design.
🔑 Features
- ESP32-S3 MCU with external QSPI Flash memory.
- Native USB 2.0 interface used for flashing and serial communication.
- MCP1826S LDO regulator:
- Low quiescent current (IQ) for low-power operation.
- Stable supply for ESP32-S3 core.
- 4-layer PCB:
- Designed and manufactured at NextPCB (PCBA service).
- Optimized for low EMI/EMC compliance.
- Castellated edges for easy integration into other projects.
- Full pinout exposure:
- All ESP32-S3 GPIOs available, including those not accessible on standard WROOM modules.
- Enables advanced use cases and maximum flexibility.
- On-board PCB antenna (MIFA type):
- Designed with uSimmics EM simulation.
- Verified radiation characteristics, dispersion parameters, and stack-up effects.
📂 Repository structure
- /schematic/ → Circuit schematic files.
- /pcb/ → PCB layout and Gerber files.
- /doc/ → Antenna simulation results and design notes.
- /firmware/ → Demo applications and example projects.
- /img/ → PCB renders and prototype photos.
🧪 Demo Application
Included with this project is a human presence detection demo based on Wi-Fi spectral footprint analysis:
- Scans available Wi-Fi networks, collecting SSID and RSSI values.
- Applies a probability-based algorithm using normal distribution to estimate environmental changes.
- Detects human presence based on variations in the Wi-Fi spectrum.
- Displays results on a color IPS display.
This demonstrates the potential of the ESP32-S3 in low-cost sensing and AIoT applications without requiring dedicated motion sensors.
🔧 Typical applications
- Low-power IoT node development.
- Integration into custom PCBs via castellated module design.
- Edge-AI and machine learning applications.
- Antenna research and RF prototyping.
📑 References
📸 Project preview
(Insert here PCB renders, antenna simulation plots, and prototype photos)
⚖️ License
This project is released under the MIT License.
You are free to use, modify, and distribute it, provided that proper credit is given.