Description
The ESP32-C3 Development Board is a powerful, cost-effective, and energy-efficient microcontroller solution for IoT, smart devices, and embedded applications. Built on the Espressif ESP32-C3 SoC, it features dual-mode Wi-Fi + Bluetooth 5 connectivity, high-performance processing, and multiple peripheral interfaces, making it ideal for prototyping, DIY projects, and commercial IoT solutions.
Key Features
- Wi-Fi + Bluetooth 5.0 (Dual Mode): Reliable wireless communication for IoT applications.
- High-Performance SoC: ESP32-C3 RISC-V single-core processor up to 160 MHz with 384KB ROM + 400KB SRAM.
- USB Type-C Connector: Simplifies programming, power delivery, and serial communication.
- External 4MB SPI Flash: Ensures stable storage, reliable upgrades, and extended application support.
- Rich Peripheral Interfaces: UART, GPIO, SPI, I²C, ADC, PWM, and more for flexible project design.
- Strong Compatibility: Supports Arduino IDE, ESP-IDF, MicroPython, LuatOS, making it beginner- and developer-friendly.
- Compact & Lightweight: Small form factor, perfect for DIY kits, IoT nodes, and embedded systems.
Specification
| Specification | Details |
|---|---|
| Processor | ESP32-C3 RISC-V Single-Core, up to 160 MHz |
| Memory | 384KB ROM, 400KB SRAM, 4MB SPI Flash |
| Wireless | Wi-Fi 802.11 b/g/n + Bluetooth 5 (LE) |
| Interfaces | UART, SPI, I²C, GPIO, ADC, PWM |
| Connector | USB Type-C |
| Operating Systems | Arduino IDE, ESP-IDF, MicroPython, LuatOS |
| Dimensions | 2.52 × 2.48 × 0.87 in (64 × 63 × 22 mm) |
| Weight | 0.634 oz (18 g) |
| Brand / Model | Tourdeus / ESP32-C3 |
| Manufacturer | Espressif / Tourdeus |
| ASIN | B0BWMY3C3R |
| First Available | February 23, 2023 |
Application
- IoT Development: Smart home devices, sensors, and automation.
- Wireless Projects: Wi-Fi connected applications and Bluetooth LE communications.
- STEM & DIY Learning: Ideal for students, makers, and hobbyists.
- Prototyping & Product Design: Great for rapid testing of IoT solutions.
- Edge Computing & AIoT: Can be integrated into machine learning edge devices.