Parameters |
Factory Lead Time |
1 Week |
Package / Case |
900-BBGA, FCBGA |
Operating Temperature |
-40°C~100°C TJ |
Packaging |
Tray |
Published |
2016 |
Series |
Zynq® UltraScale+™ MPSoC EG |
Part Status |
Active |
Moisture Sensitivity Level (MSL) |
4 (72 Hours) |
HTS Code |
8542.31.00.01 |
Peak Reflow Temperature (Cel) |
NOT SPECIFIED |
Time@Peak Reflow Temperature-Max (s) |
NOT SPECIFIED |
Number of I/O |
204 |
Speed |
533MHz, 600MHz, 1.3GHz |
RAM Size |
256KB |
Core Processor |
Quad ARM® Cortex®-A53 MPCore™ with CoreSight™, Dual ARM®Cortex™-R5 with CoreSight™, ARM Mali™-400 MP2 |
Peripherals |
DMA, WDT |
Connectivity |
CANbus, EBI/EMI, Ethernet, I2C, MMC/SD/SDIO, SPI, UART/USART, USB OTG |
Architecture |
MCU, FPGA |
Primary Attributes |
Zynq®UltraScale+™ FPGA, 256K+ Logic Cells |
RoHS Status |
ROHS3 Compliant |
This SoC is built on Quad ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight?, ARM Mali?-400 MP2 core processor(s).
Quad ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight?, ARM Mali?-400 MP2 core processor(s) are used in the construction of this SoC.The manufacturer assigns this system on a chip with a 900-BBGA, FCBGA package as per the manufacturer's specifications.With 256KB RAM implemented, this SoC chip provides users with reliable performance.In terms of internal architecture, this SoC design uses the MCU, FPGA method.The system on a chip is part of the series Zynq? UltraScale+? MPSoC EG.It is recommended that this SoC meaning be operated at -40°C~100°C TJ on an average.A significant feature of this SoC security is the combination of Zynq?UltraScale+? FPGA, 256K+ Logic Cells.An advanced Tray package houses this SoC system on a chip.An integral part of this SoC consists of a total of 204 I/Os.
Quad ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight?, ARM Mali?-400 MP2 processor.
256KB RAM.
Built on MCU, FPGA.
There are a lot of Xilinx Inc.
XCZU5EG-2FBVB900I System On Chip (SoC) applications.
- ARM support modules
- Remote control
- Flow Sensors
- Samsung galaxy gear
- Communication interfaces ( I2C, SPI )
- Efficient hardware for inference of neural networks
- Avionics
- Deep learning hardware
- PC peripherals
- Multiprocessor system-on-chips (MPSoCs)