XCZU5EG-3FBVB900E
| Parameters | |
|---|---|
| Factory Lead Time | 1 Week |
| Package / Case | 900-BBGA, FCBGA |
| Operating Temperature | 0°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 | 600MHz, 1.5GHz |
| RAM Size | 256KB |
| Core Processor | Quad 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, 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? core processor(s).
This SoC is built on Quad ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight? core processor(s).The manufacturer assigns this system on a chip with a 900-BBGA, FCBGA package.A SoC chip with 256KB RAM is provided for users to enjoy reliable performance.A MCU, FPGA technique is used for the SoC design's internal architecture.A system on chip SoC of this type belongs to the Zynq? UltraScale+? MPSoC EG series.For this SoC meaning, the average operating temperature should be 0°C~100°C TJ.One important thing to mark down is that this SoC meaning combines Zynq?UltraScale+? FPGA, 256K+ Logic Cells.Tray package houses this SoC system on a chip.In total, this SoC part has 204 I/Os.
Quad 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.
XCZU5EG-3FBVB900E System On Chip (SoC) applications.
- Networked Media Encode/Decode
- Level
- Functional safety for critical applications in the aerospace
- Networked sensors
- Efficient hardware for training of neural networks
- System-on-chip (SoC)
- Smartphones
- Robotics
- Medical
- Smart appliances
