Parameters |
Factory Lead Time |
11 Weeks |
Package / Case |
784-BFBGA, 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 |
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, 154K+ 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).
A core processor Quad ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight?, ARM Mali?-400 MP2 is used to build this SoC.Manufacturer assigns package 784-BFBGA, FCBGA to 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 utilizes the MCU, FPGA technique.The Zynq? UltraScale+? MPSoC EG series contains this system on chip SoC.It is expected that this SoC meaning will operate at 0°C~100°C TJ on average.A significant feature of this SoC security is the combination of Zynq?UltraScale+? FPGA, 154K+ Logic Cells.There is a state-of-the-art Tray package that houses this SoC system on a chip.An integral part of this SoC consists of a total of 252 I/Os.
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.
XCZU3EG-1SFVC784E System On Chip (SoC) applications.
- Digital Signal Processing
- Samsung galaxy gear
- USB hard disk enclosure
- Efficient hardware for inference of neural networks
- CNC control
- Medical
- Sensor network-on-chip (sNoC)
- Measurement tools
- POS Terminals
- POS Terminals