M2S090TS-1FGG484T2
| Parameters | |
|---|---|
| Factory Lead Time | 10 Weeks |
| Lifecycle Status | IN PRODUCTION (Last Updated: 1 month ago) |
| Package / Case | 484-BGA |
| Operating Temperature | -40°C~125°C TJ |
| Packaging | Tray |
| Published | 2016 |
| Series | Automotive, AEC-Q100, SmartFusion®2 |
| Pbfree Code | yes |
| Part Status | Active |
| Moisture Sensitivity Level (MSL) | 3 (168 Hours) |
| Peak Reflow Temperature (Cel) | NOT SPECIFIED |
| Time@Peak Reflow Temperature-Max (s) | NOT SPECIFIED |
| Number of I/O | 267 |
| 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 |
| Programmable Logic Type | FIELD PROGRAMMABLE GATE ARRAY |
| Primary Attributes | FPGA - 90K Logic Modules |
| Flash Size | 512KB |
| RoHS Status | RoHS Compliant |
This SoC is built on ARM? Cortex?-M3 core processor(s).
This SoC is built on ARM? Cortex?-M3 core processor(s).Package 484-BGA is assigned to this system on a chip by the manufacturer.A SoC chip with 64KB RAM is provided for users to enjoy reliable performance.Using the MCU, FPGA technique, this SoC design's internal architecture is simple.Automotive, AEC-Q100, SmartFusion?2 is the series name of this system on chip SoC.It is expected that this SoC meaning will operate at -40°C~125°C TJ on average.This SoC security combines FPGA - 90K Logic Modules, an important feature to keep in mind.A state-of-the-art Tray package houses this SoC system on a chip.In total, this SoC part has 267 I/Os.The configuration of FIELD PROGRAMMABLE GATE ARRAY can be adapted to meet the needs of different design projects.As for its flash size, it is 512KB.
ARM? Cortex?-M3 processor.
64KB RAM.
Built on MCU, FPGA.
512KB extended flash.
There are a lot of Microsemi Corporation
M2S090TS-1FGG484T2 System On Chip (SoC) applications.
- Transmitters
- Avionics
- Keyboard
- External USB hard disk/SSD
- Remote control
- Smart appliances
- Smartphones
- Efficient hardware for inference of neural networks
- Medical
- Special Issue Editors