XCZU2CG-1SFVA625E
| Parameters | |
|---|---|
| Factory Lead Time | 11 Weeks |
| Package / Case | 625-BFBGA, FCBGA |
| Operating Temperature | 0°C~100°C TJ |
| Packaging | Tray |
| Published | 2016 |
| Series | Zynq® UltraScale+™ MPSoC CG |
| JESD-609 Code | e1 |
| Part Status | Active |
| Moisture Sensitivity Level (MSL) | 4 (72 Hours) |
| Terminal Finish | Tin/Silver/Copper (Sn96.5Ag3.0Cu0.5) |
| HTS Code | 8542.31.00.01 |
| Peak Reflow Temperature (Cel) | NOT SPECIFIED |
| Time@Peak Reflow Temperature-Max (s) | NOT SPECIFIED |
| Number of I/O | 180 |
| 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).
A core processor Dual ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight? is used to build this SoC.Manufacturer assigns package 625-BFBGA, FCBGA to this system on a chip.Users will enjoy reliable performance with this SoC chip, which has implemented 256KB RAM.When it comes to internal architecture, this SoC design employs the MCU, FPGA technique.Zynq? UltraScale+? MPSoC CG is the series name of this system on chip SoC.Typical operating temperatures for this SoC meaning should be 0°C~100°C TJ.There is one thing to note about this SoC security: it combines Zynq?UltraScale+? FPGA, 103K+ Logic Cells.A state-of-the-art Tray package houses this SoC system on a chip.An integral part of this SoC consists of a total of 180 I/Os.
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-1SFVA625E System On Chip (SoC) applications.
- Efficient hardware for inference of neural networks
- String inverter
- Microcontroller based SoC ( RISC-V, ARM)
- Digital Signal Processing
- Functional safety for critical applications in the automotive
- Communication network-on-Chip (cNoC)
- Automated sorting equipment
- Smartphones
- ARM processors
- Mobile computing