Parameters |
Factory Lead Time |
1 Week |
Package / Case |
784-BFBGA, FCBGA |
Operating Temperature |
0°C~100°C TJ |
Packaging |
Tray |
Series |
Zynq® UltraScale+™ MPSoC CG |
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 |
252 |
Speed |
500MHz, 1.2GHz |
RAM Size |
256KB |
Core Processor |
Dual ARM® Cortex®-A53 MPCore™ with CoreSight™, Dual ARM®Cortex™-R5 with CoreSight™ |
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, 103K+ Logic Cells |
RoHS Status |
ROHS3 Compliant |
This SoC is built on Dual ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight? core processor(s).
On this SoC, there is Dual ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight? core processor.According to the manufacturer, this system on a chip has a package of 784-BFBGA, FCBGA.A SoC chip with 256KB RAM is provided for users to enjoy reliable performance.As far as its internal architecture is concerned, this SoC design employs the MCU, FPGA technique.Featured system on chip SoCs of the Zynq? UltraScale+? MPSoC CG series.As a rule of thumb, the average operating temperature for this SoC meaning should be 0°C~100°C TJ.A key point to note is that this SoC security combines Zynq?UltraScale+? FPGA, 103K+ Logic Cells.An advanced Tray package houses this SoC system on a chip.As a whole, this SoC part is comprised of 252 inputs and outputs.
Dual ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight? processor.
256KB RAM.
Built on MCU, FPGA.
There are a lot of Xilinx Inc.
XCZU2CG-1SFVC784E System On Chip (SoC) applications.
- Multiprocessor system-on-chips (MPSoCs)
- Self-aware system-on-chip (SoC)
- Special Issue Information
- Optical drive
- Industrial AC-DC
- Healthcare
- Functional safety for critical applications in the aerospace
- Fitness
- Deep learning hardware
- Microcontroller based SoC ( RISC-V, ARM)