Photonic Computing: Revolutionizing Signal Processing with Light-Based Circuits

Photonic computing and analog signal processing are emerging fields that use light properties to perform computations and process signals. Photonic circuits offer a large operation bandwidth, low power consumption, and fine frequency control, enabling a wide range of computational techniques. These circuits are also capable of massively parallelized computations through wavelength-division multiplexing (WDM).

Photonic circuits are changing the computing landscape by offering higher bandwidth than digital computers, reducing the need for space-consuming and power-hungry parallelism. Applications of photonic circuits include optical steganography and photonic blind-source separation, which manage interference in wireless communication systems and disguise sensitive communications, respectively.

What is Photonic Computing and Analog Signal Processing?

Photonic computing and analog signal processing are emerging fields that leverage the properties of light to perform computations and process signals. Photonic computational circuits have a large operation bandwidth, low power consumption, and fine frequency control. These properties enable a wide range of application-specific computational techniques that are impossible to implement using traditional electrical and digital hardware alone.

Photonic circuits are tools that enable a wide variety of applications in the domain of analog signal processing. The low power consumption and high bandwidth of these circuits allow for the implementation of operations from basic arithmetic to frequency-domain manipulation at speeds and efficiencies that their electrical counterparts are unable to approach. This high bandwidth also enables massively parallelized computations through the use of wavelength-division multiplexing (WDM).

The analog nature of photonic circuits allows for signal processing in continuous time and reduces the cost, memory requirements, and precision loss resulting from the need to digitize massive amounts of data. These advantages are not only beneficial for applications involving optical signals, such as optical fiber or free space optical communication, but they also show promise when applied to computation in general.

How are Photonic Circuits Changing the Landscape of Computing?

In recent years, there has been a push toward implementing physical layer solutions to problems that are currently handled in software. The rapid reconfigurability and deterministic operation of digital electronics make it appealing as a foundation for solving any engineering problem, but it is not always the optimal solution. The discrete nature of computers forces quantization, which adds a small amount of noise to the measured signals. It also forces discretization of incoming signals, which can drive up the price of circuitry in high-frequency applications and add latency to systems that may need to operate in real time.

Photonic circuits, on the other hand, have drastically higher bandwidth than digital computers. This means that some applications will force space-consuming and power-hungry parallelism to meet performance requirements, while some that are not easily parallelizable become functionally impossible after a certain threshold for speed requirements is crossed. Converting solutions that are suboptimally implemented by computers to the physical layer removes these limitations and leverages the unique advantages of the circuit paradigm to make elegant work of complex, difficult, and performance-sensitive programming tasks.

What are the Applications of Photonic Circuits?

Photonic circuits achieve many useful effects through the strategic splitting and combining of signals. Linear mixing through coupling or over-air transmission provides an avenue for technologies like optical steganography, which aims to hide communication signals by combining them with noise. This phenomenon also causes interference in communication lines, prompting the exploration of using photonic blind-source separation to recover signals as they were before mixing.

Combining these linear operations with nonlinearities caused by excitable lasers or resonator-based modulators allows for the creation of photonic neural networks. This effectively allows any function to be created so long as the hardware requirements can be met. All of these capabilities show potential for improving high-speed communication systems as they require throughputs that would overwhelm most analog electrical circuitry and drive up the price of digital signal processing techniques through increasingly high-speed analog-to-digital converters.

What is Photonic Blind-Source Separation?

Photonic blind-source separation is a technique that uses photonic circuits to manage interference in wireless communication systems, free-space optical communication systems, and optics-based sensor technologies. It takes advantage of the rich amplitude information present in multi-input-multi-output signal processing systems to separate signals that exist in the same electromagnetic bandwidth, circumventing the limitations of traditional frequency filtration and multiplexing techniques.

Photonic blind-source separation was first demonstrated in 2018, where the high throughput of photonic arithmetic circuitry was leveraged to separate communication band RF signals in real time. Since then, several techniques for improving the method have been explored, such as sub-Nyquist optical pulse sampling, genetic algorithms for PC-IC searching, and hybrid signal domain systems for increasing the likelihood of separability.

What is Optical Steganography?

Optical steganography is another technique that is built upon the advantages of photonic signal processing circuits. This technique aims to disguise sensitive communications as system noise, preventing eavesdroppers from interpreting, recording, or potentially even noticing them. It takes advantage of inexpensive, easily implemented photonic signal processing circuits to achieve this goal.