Memory - Configuration Proms for FPGAs

Part Number	Description / PDF	Quantity
XCF16PVO48C0973 Xilinx	CONFIG MEMORY, 16MX1, SERIAL	54055
XCF02SVO20C Xilinx	IC PROM IN SYST PRG 3.3V 20TSSOP	877
EPC1064VLC20 Altera (Intel)	CONFIG MEMORY, 64KX1, SERIAL	392
AT17F040-30JU Roving Networks / Microchip Technology	IC FLASH CONFIG 4M 20PLCC	108
XC1736EPC20C Xilinx	CONFIG MEMORY, 36288X1, SERIAL	1135
XC18V01PC20C Xilinx	IC PROM SERIAL CONFIG 1M 20-PLCC	0
AT17LV002-10CU Roving Networks / Microchip Technology	IC SRL CONFIG EEPROM 2M 8-LAP	0
XC18V512VQG44C Xilinx	IC PROM REPROGR 512KB 44-VQFP	0
AT17LV65-10CC Roving Networks / Microchip Technology	IC SRL CONFIG EEPROM 64K LV 8LAP	120
AT17LV512A-10JC Atmel (Microchip Technology)	CONFIG MEMORY, 512KX1, SERIAL	831
AT17LV128-10JI Atmel (Microchip Technology)	CONFIG MEMORY, 128KX1	507
XC1702LVQ44C Xilinx	CONFIG MEMORY, 2MX1, SERIAL	33
XCF128XFT64C Xilinx	IC PROM SRL 128M GATE 64-FTBGA	0
XC1704LVQ44C Xilinx	CONFIG MEMORY, 4MX1, SERIAL	0
EPC1064VTC32 Altera (Intel)	CONFIG MEMORY, 64KX1, SERIAL	636
XCF32PFS48C Xilinx	IC PROM SRL/PAR 1.8V 32M 48CSBGA	0
EPC1064TC32 Altera (Intel)	CONFIG MEMORY, 64KX1, SERIAL	1386
XCF16PFS48C Xilinx	IC PROM SRL 1.8V 16M 48CSBGA	0
XCF08PFS48C Xilinx	IC PROM SRL 1.8V 8M GATE 48CSBGA	0
XCF16PVO48C Xilinx	CONFIG MEMORY, 16MX1, SERIAL	0

Memory - Configuration Proms for FPGAs

1. Overview

Configuration PROMs (Programmable Read-Only Memory) for FPGAs are non-volatile memory devices designed to store configuration data for Field-Programmable Gate Arrays (FPGAs). These devices enable FPGAs to retain their programmed logic functionality after power cycling. Modern applications demand reliable, high-speed, and secure storage solutions for FPGA configurations in aerospace, telecommunications, automotive, and industrial systems.

2. Main Types and Functional Classification

Type	Functional Characteristics	Application Examples
OTP PROMs	One-Time Programmable, low cost, high reliability	Industrial control systems
Flash PROMs	Reprogrammable, high density, moderate endurance	5G base stations
EEPROM	Byte-alterable, high endurance, slower access speed	Medical imaging equipment
FRAM	Low-power, radiation-hardened, fast write speed	Aerospace avionics

3. Structure and Composition

Typical configuration PROMs feature:

Package types: TSSOP, VQFN, BGA
Memory array: Floating-gate or antifuse-based technology
Interface: SPI, BPI, or parallel bus
Voltage rails: 1.2V-3.3V operation with decoupling capacitors
Error correction: Built-in ECC for radiation environments

4. Key Technical Specifications

Parameter	Significance
Memory density (1Mb-4Gb)	Determines maximum FPGA configuration size
Access time (55ns-120ns)	Impacts system boot speed
Endurance (10k-100k cycles)	Dictates reprogramming lifespan
Data retention (20-100 years)	Critical for long-term reliability
Radiation hardness (SEL/SEU immunity)	Essential for space applications

5. Application Areas

Primary application domains:

Telecommunications: 5G NR base stations, optical transceivers
Industrial: Programmable logic controllers (PLCs)
Medical: MRI scanners, patient monitoring systems
Automotive: ADAS sensor fusion units
Defense: Radar signal processing systems

6. Key Manufacturers and Products

Manufacturer	Product Series	Key Features
Microchip	AT17F Series	128Mb SPI interface, -40 C to +125 C
Intel	EP180S	Secure boot, TSMC 16nm process
Xilinx	S25FL-V	Octal SPI, 400MHz clock rate
Cypress	FL-S Series	End-to-end data protection

7. Selection Guidelines

Key selection factors:

Match memory density to target FPGA's bitstream size
Verify interface compatibility (SPI/BPI/parallel)
Evaluate environmental requirements (temperature, radiation)
Assess security features (encryption, authentication)
Consider programming infrastructure (socket compatibility)

8. Industry Trends

Current development trends include:

Transition to 3D NAND architecture for densities beyond 8Gb
Integration of hardware security modules (HSM)
Adoption of JEDEC Xccela interface for >400MB/s throughput
Development of radiation-tolerant devices for LEO satellites
Implementation of AI-driven wear-leveling algorithms