XCZU9CG-2FFVB1156E
| Parameters | |
|---|---|
| Factory Lead Time | 1 Week |
| Package / Case | 1156-BBGA, FCBGA |
| Operating Temperature | 0°C~100°C TJ |
| Packaging | Tray |
| Published | 2013 |
| Series | Zynq® UltraScale+™ MPSoC CG |
| Pbfree Code | yes |
| 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 | 328 |
| Speed | 533MHz, 1.3GHz |
| 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, 599K+ 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).
Based on the core processor(s) Dual ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight?, this SoC has been developed.Manufacturer assigns package 1156-BBGA, FCBGA to this system on a chip.A SoC chip with 256KB RAM is provided for users to enjoy reliable performance.When it comes to internal architecture, this SoC design employs the MCU, FPGA technique.It is part of the Zynq? UltraScale+? MPSoC CG series of system on a chips.For this SoC meaning, the average operating temperature should be 0°C~100°C TJ.A significant feature of this SoC security is the combination of Zynq?UltraScale+? FPGA, 599K+ Logic Cells.Tray package houses this SoC system on a chip.328 I/Os in total are included in this SoC part.
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.
XCZU9CG-2FFVB1156E System On Chip (SoC) applications.
- Industrial robot
- Multiprocessor system-on-chips (MPSoCs)
- Published Paper
- Efficient hardware for inference of neural networks
- Smart appliances
- Microcontroller
- Fitness
- Wireless networking
- Healthcare
- Three phase UPS
