Parameters |
Package / Case |
896-BGA |
Supplier Device Package |
896-FBGA (31x31) |
Operating Temperature |
-40°C~100°C TJ |
Packaging |
Tray |
Published |
2016 |
Series |
SmartFusion®2 |
Part Status |
Obsolete |
Moisture Sensitivity Level (MSL) |
3 (168 Hours) |
Max Operating Temperature |
100°C |
Min Operating Temperature |
-40°C |
Frequency |
166MHz |
Base Part Number |
M2S050S |
Interface |
CAN, Ethernet, I2C, SPI, UART, USART, USB |
Number of I/O |
377 |
Speed |
166MHz |
RAM Size |
64KB |
Core Processor |
ARM® Cortex®-M3 |
Peripherals |
DDR, PCIe, SERDES |
Connectivity |
CANbus, Ethernet, I2C, SPI, UART/USART, USB |
Architecture |
MCU, FPGA |
Core Architecture |
ARM |
Primary Attributes |
FPGA - 50K Logic Modules |
Flash Size |
256KB |
RoHS Status |
Non-RoHS Compliant |
This SoC is built on ARM? Cortex?-M3 core processor(s).
A core processor ARM? Cortex?-M3 is used to build this SoC.The manufacturer assigns this system on a chip with a 896-BGA package.The SoC chip provides users with reliable performance because it is implemented with 64KB RAM.This SoC design employs the MCU, FPGA technique for its internal architecture.In the SmartFusion?2 series, this system on chip SoC is included.It is expected that this SoC meaning will operate at -40°C~100°C TJ on average.It is important to note that this SoC security combines FPGA - 50K Logic Modules.Tray package houses this SoC system on a chip.This SoC part has a total of 377 I/Os.A flashing 256KB appears on it.M2S050S can help you find system on chips with similar specs and purposes.At 166MHz, the wireless SoC works.The SoC meaning is based on the core architecture of ARM.It is just enough to start the SoC computing at -40°C.In this SoC system on chip, 100°C represents its maximum operating temperature.
ARM? Cortex?-M3 processor.
64KB RAM.
Built on MCU, FPGA.
256KB extended flash.
Core Architecture: ARM
There are a lot of Microsemi Corporation
M2S050S-1FG896I System On Chip (SoC) applications.
- Microcontroller based SoC ( RISC-V, ARM)
- ARM processors
- Efficient hardware for training of neural networks
- Avionics
- Robotics
- String inverter
- Defense
- RISC-V
- Cyberphysical system-on-chip
- Deep learning hardware