FPGA Applications in Photonics: Classical and Quantum Technologies

Introduction

Field-Programmable Gate Arrays (FPGAs) are reconfigurable integrated circuits that can be programmed to implement custom hardware logic. Unlike fixed-function ASICs or software running on CPUs/GPUs, FPGAs consist of an array of configurable logic blocks (e.g. lookup tables and flip-flops) with programmable interconnects, plus dedicated resources like DSP cores and memory blocks. This architecture enables massive parallelism and deterministic timing in signal processing‎[2]. In photonics – the science and technology of light – many applications demand precise timing (often sub-nanosecond jitter), high throughput data handling, and real-time processing that outpace general-purpose processors. FPGAs excel in these aspects by offering nanosecond-scale latency, hardware-level concurrency, and flexibility to interface with fast analog/digital converters and optical transceivers. This report explores the best uses of FPGAs in photonics, spanning classical electro-optic systems and emerging quantum technologies. We begin by explaining key FPGA attributes (architecture and jitter) and why they are beneficial for photonics. We then examine a range of photonics application areas – from high-speed optical communications and LIDAR to ultrafast lasers, spectroscopy, adaptive optics, and imaging – highlighting specific FPGA-based implementations from academia, laboratories, and industry. Finally, we review the role of FPGAs in quantum photonic systems, including quantum sensing, quantum communication (e.g. QKD), and quantum computing interfaces, with real-world examples of their use in photon counting and qubit control. Throughout, recent demonstrations (primarily from the last 5–7 years) are cited to illustrate state-of-the-art achievements.

FPGA Architecture, Timing Jitter, and Benefits for Photonics

FPGA Architecture: An FPGA is essentially a sea of logic gates that the user can wire together in nearly arbitrary ways. Modern FPGAs contain thousands of logic elements organized into configurable logic blocks (CLBs), each with lookup tables to implement boolean functions and flip-flops for storage, all connected via programmable routing fabric. They also include embedded memory (Block RAM), hardware multipliers/accumulators (DSP slices), and high-speed I/O transceivers. This architecture allows implementing custom digital circuits optimized for a specific task, and these circuits operate in true parallel fashion (every logic element can work concurrently on different bits of data) as opposed to sequential execution in CPUs ‎[2]. For photonics, this means an FPGA can process multiple high-speed optical data streams or sensor signals simultaneously (e.g. multi-channel photodetectors or pixel arrays), and can perform pipelined computations on each clock cycle. Crucially, FPGA designs are synchronous – driven by a clock – which gives precise control over timing. Once configured, signal propagation through the FPGA’s logic occurs with fixed, known delays (on the order of nanoseconds), enabling deterministic response timing that is critical in feedback loops and timing-sensitive optical experiments.

Timing Jitter: Jitter refers to the small timing fluctuations or uncertainty in event occurrence times, often measured as the standard deviation of a signal’s timing error. In photonic systems, jitter in a clock or trigger can degrade performance – for example, timing jitter in a pulsed laser driver broadens the pulses, and jitter in detector gating or timestamping limits time-of-flight accuracy. FPGAs can help minimize and manage jitter in two ways. First, by bringing processing into hardware, they avoid the indeterminate latency of software and operating systems; an FPGA can respond to an input event within one clock cycle deterministically, whereas a microcontroller or PC might have unpredictable scheduling delays. Second, FPGAs can incorporate custom time-to-digital converters (TDCs) and phase-locked loops to measure and adjust timing with picosecond resolution. Modern FPGA-based time interval analyzers achieve digital time resolutions below 1 picosecond – for instance, one system reports a 780 fs timing resolution with an RMS jitter under 20 ps (​liquidinstruments ‎[7]). Such precise timing is extremely useful for photon counting and optical ranging applications. Additionally, multiple signals can be aligned or correlated in hardware with sub-nanosecond precision on an FPGA, which is very challenging via software. By using techniques like tapped delay lines in FPGA fabric, time intervals much shorter than the base clock period can be resolved, providing fine-grained control and measurement of optical pulse timing (​liquidinstruments ‎[7]).  Overall, the low intrinsic jitter and deterministic timing of FPGAs make them ideal for synchronization tasks (e.g. locking laser pulses to detectors) and any photonic system where timing stability is paramount.

Benefits in Photonics: The general advantages of FPGAs – reconfigurability, parallel processing, and low-latency control – map strongly to photonics needs. Many optical systems generate high data throughput (for example, gigabit-per-second serial data in optical communication, or megapixel cameras in imaging) which FPGAs can handle by distributing the workload across many logic elements or pipeline stages. Photonics experiments often must run in real-time, such as adaptive optics systems that correct a laser beam while it propagates, or a spectroscopy setup that adjusts in real-time to a changing signal. An FPGA can implement closed-loop controllers or signal processors that update every microsecond or faster, which is far beyond the capability of PC-based control. The hardware-level parallelism also allows complex algorithms (filtering, FFTs, neural networks, etc.) to be executed with deterministic throughput regardless of algorithmic complexity, so long as the design fits in the FPGA. This is critical in timing-sensitive applications: e.g., in a pulsed laser system, an FPGA can monitor each pulse and apply a correction or log an event with exactly the same delay each time, ensuring a stable timing relationship (minimal drift). In summary, using FPGAs in photonics yields: (1) Precise timing and low jitter for generating and measuring optical events, (2) High-speed signal processing close to the source (avoiding data bottlenecks of transferring to a PC), and (3) Reconfigurable logic that can be tailored to specific photonic experiments or standards, and updated as requirements evolve. These benefits have motivated widespread adoption of FPGAs in both classical optical systems and cutting-edge quantum photonic setups.

High-Speed Optical Communication Systems

One of the most significant classical photonics application areas for FPGAs is high-speed optical communication. Fiber-optic communication links (and free-space optical links) often operate at tens of gigabits per second and require sophisticated digital signal processing (DSP) for modulation, demodulation, and error correction. FPGAs are frequently used as the digital engine in optical transceivers and research prototypes because they can keep up with the required data rates and implement custom algorithms. For example, in intensity-modulation direct-detection links using advanced modulation formats like PAM4 (4-level Pulse Amplitude Modulation), FPGA-based processors can pre-compensate and equalize nonlinearities in real-time. Hu et al. (2024) demonstrated a 24.576 Gbit/s short-reach optical link using an FPGA to implement a neural-network-based digital pre-distortion (DPD) algorithm ‎[2].​ In their system, a 14.7456 GBaud PAM4 optical signal (20 km fiber link) is processed by a 64-channel parallel multilayer perceptron on the FPGA, which pre-distorts the transmitter waveform to counteract fiber and device nonlinearities. This FPGA-based approach kept the bit error rate below the forward error correction threshold, achieving error-free communication at 24+ Gbps‎.​ The authors note that “Field-programmable gate arrays, with their real-time processing capability, high parallelism, and flexible programming features, are ideal hardware choices for meeting the demands of high-speed data transmission in optical communications.”​ ‎[2]. Indeed, many optical communication experiments rely on FPGA boards for real-time DSP: tasks include carrier recovery and demodulation in coherent optical receivers, forward error correction coding/decoding, and framing at 100 Gbps and beyond. While commercial optical transceivers eventually use ASICs for these functions (to reduce cost per unit), FPGAs are indispensable in research and prototype stages due to their programmability and rapid development cycle – allowing testing of new modulation formats or DSP algorithms in working optical links.

Another niche in optical communications where FPGAs shine is free-space and underwater optical links, which often have unique modulation or synchronization schemes. For instance, researchers demonstrated an FPGA-based design for a 4 Gbps low-latency underwater optical communication system, showing that the FPGA could handle the tight timing for half-duplex transmission and quick turnaround between transmit/receive modes​‎[2]. In Visible Light Communication (VLC) or optical wireless systems, FPGAs can perform real-time adaptation to changing channel conditions (like modulation adjustments based on ambient light), which would be too slow if done in software. In summary, FPGAs provide the muscle for high-throughput optical communication by executing custom, parallel DSP pipelines – from filtering and equalization to clock/data recovery – all with deterministic low latency. This makes them critical for achieving the requisite performance in systems such as coherent fiber links, short-range optical interconnects, and advanced optical modulation research.

LIDAR and Time-of-Flight Ranging

Light Detection and Ranging (LIDAR) systems send out laser pulses or chirped optical signals and measure reflections to determine distance or create 3D images of environments. FPGAs are widely used in LIDAR for both signal generation (timing the outgoing pulses or chirps) and signal processing (capturing return signals and computing distances) in real-time. A key challenge in LIDAR is timing accuracy – for a pulsed Time-of-Flight (ToF) LIDAR, measuring distances with centimeter accuracy requires timing optical pulse returns with on the order of 100 ps precision. FPGAs, with their low jitter and ability to implement high-resolution TDCs, are ideal for this. They can timestamp the departure and arrival of laser pulses and compute distances on the fly. Additionally, parallel processing is useful in LIDAR for handling multiple detection channels or multiple pulses in flight. Frequency-Modulated Continuous-Wave (FMCW) LIDAR is an advanced form that uses chirped (frequency-swept) lasers and measures distance via frequency shift of the beat signal. FMCW LIDAR offers high sensitivity and can measure velocity (via Doppler) but requires heavy signal processing (Fourier transforms to extract beat frequencies) at high data rates. FPGAs have been crucial in making FMCW LIDAR feasible by handling these computations in real-time. Kim et al. (2020) developed an FPGA-based FMCW LIDAR processing engine that significantly reduces hardware complexity while improving range resolution‎ [3]. In their design, the analog optical front-end outputs a high-frequency beat signal (proportional to target distance); instead of performing a large 8192-point FFT in one go (which is hardware intensive), they use a digital down-conversion (DDC) technique in the FPGA to lower the sampling rate and then perform a 256-point FFT [3]. By doing so, they achieved distance measurements in 3 cm increments with an RMS error of about 3 cm, and further applied a constant false-alarm rate (CFAR) algorithm on the FPGA to improve the ranging precision to ~1.9 cm RMS​. All signal processing – including mixers, decimation filters, power estimation, FFT, and peak detection – was implemented in the FPGA’s logic and Block RAM. Notably, the entire hardware module was verified on a Xilinx Zynq FPGA, and the approach could handle beat frequencies corresponding to distances up to 50 m at fine resolution​ [3]. This level of real-time performance would be extremely challenging without FPGAs; a CPU processing 8192-point FFTs at multi-megahertz rates would not keep up, whereas the FPGA can pipeline the operations.