Shenzhen Meiyuxin Technology Co., Ltd. [distributing IoT chips] sells ADUCM3029BCPZ ultra-low power consumption ARM Cortex-M3 MCU with integrated power management and 256Kb embedded flash memory. Quality assurance New original! ! !

Product Description:

The ADUCM3029BCPZ microcontroller units (MCUs) are ultra-low-power microcontroller systems with integrated power management for processing, control, and connectivity. The MCU system is based on the ARM® Cortex®-M3 processor and consists of digital peripherals, embedded SRAM and flash memory, an analog subsystem that provides clock, reset, and power management functions, and an analog-to-digital converter (ADC) subsystem. A comparison of the characteristics of different ADuCM3027/ADuCM3029 products.

System features shared by the ADUCM3029BCPZ MCUs include:

ARM Cortex-M3 processor up to 26 MHz

Up to 256 kB embedded flash with integrated error correcting code (ECC)

Optional 4 kB cache (to reduce active power)

64 kB system SRAM with parity

Power Management Unit (PMU)

Multilayer Advanced Microcontroller Bus Architecture (AMBA) bus matrix

Central direct memory access (DMA) controller

Buzzer interface

Serial Port (SPORT), Serial Peripheral Interface (SPI), Inter-Integrated Circuit (I2C), and Universal Asynchronous Receiver/Transmitter (UART) peripheral interfaces

Integrated encryption hardware support for Advanced Encryption Standard (AES) and Secure Hash Algorithm (SHA)-256

Real Time Clock (RTC)

General Purpose and Supervisory Timers

Programmable general-purpose input/output (GPIO) pins

Hardware cyclic redundancy check (CRC) calculator with built-in programmable generator polynomial

Power-on reset (POR) and power supply monitor (PSM)

12-bit Successive Approximation Register (SAR) ADC

True Random Number Generator (TRNG)

Application:

Internet of Things (IoT)

Electronic Shelf Labels (ESL) and Labeling

smart infrastructure

smart Lock

asset tracking

Smart machines, smart metering, smart buildings, smart cities and smart agriculture

Wearable device

Fitness and Clinical

Machine Learning and Neural Networks