Parameters |
Factory Lead Time |
1 Week |
Package / Case |
784-BFBGA, 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 |
252 |
Speed |
500MHz, 600MHz, 1.2GHz |
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, 192K+ 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.It has been assigned a package 784-BFBGA, FCBGA by its manufacturer for this system on a chip.This SoC chip is equipped with 256KB RAM and guarantees reliable performance to the user.The internal architecture of this SoC design is based on the MCU, FPGA technique.It is part of the Zynq? UltraScale+? MPSoC EG series of system on a chips.This SoC meaning should have an average operating temperature of -40°C~100°C TJ when it is operating normally.It is important to note that this SoC security combines Zynq?UltraScale+? FPGA, 192K+ Logic Cells.It is packaged in a state-of-the-art Tray package.As a whole, this SoC part is comprised of 252 inputs and outputs.
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.
XCZU4EG-1SFVC784I System On Chip (SoC) applications.
- Cyberphysical system-on-chip
- Industrial automation devices
- Industrial
- CNC control
- Published Paper
- Medical
- Efficient hardware for training of neural networks
- Medical
- sequence controllers
- Industrial sectors