Optical Computing: Status and Perspectives

Abstract

For many years, optics has been employed in computing, although the major focus has been and remains to be on connecting parts of computers, for communications, or more fundamentally in systems that have some optical function or element (optical pattern recognition, etc.). Optical digital computers are still evolving; however, a variety of components that can eventually lead to true optical computers, such as optical logic gates, optical switches, neural networks, and spatial light modulators have previously been developed and are discussed in this paper. High-performance off-the-shelf computers can accurately simulate and construct more complicated photonic devices and systems. These advancements have developed under unusual circumstances: photonics is an emerging tool for the next generation of computing hardware, while recent advances in digital computers have empowered the design, modeling, and creation of a new class of photonic devices and systems with unparalleled challenges. Thus, the review of the status and perspectives shows that optical technology offers incredible developments in computational efficiency; however, only separately implemented optical operations are known so far, and the launch of the world’s first commercial optical processing system was only recently announced. Most likely, the optical computer has not been put into mass production because there are still no good solutions for optical transistors, optical memory, and much more that acceptance to break the huge inertia of many proven technologies in electronics.

Polarization-Sensitive Patterning of Azopolymer Thin Films Using Multiple Structured Laser Beams

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Decentralized power grid control scheme utilizing photonic sensing and computing

“…The latency of photonic integrated circuits or free-space computations varies by implementation, but the fundamental physical limit is low. This detachment from Moore's Law is occurring in specialized applications, 16 with promising carry-overs to the hardest of computational problems. 17 As machine learning frameworks expand to meet the future needs of grid control, the complexity of neural network architecture grow, stunning their prediction speed.…”

All‐Optical Nonlinear Neuron Based on Metallic Quantum Wells

“…[ 32–34 ] Considering the restriction of field‐enhancement and requirement on much more complicated nanostructures, a more straightforward way is directly improving the Kerr nonlinear coefficient. Recently, the large nonlinearity enabled by semiconductor quantum wells, [ 35,36 ] 2D layered materials, [ 7–37 ] and metallic quantum wells (MQWs) [ 38,39 ] has demonstrated a new route for this direction. Especially, the giant Kerr nonlinearities from MQWs provide a novel mechanism for physical breakthrough and practical implementation of optical nonlinear neurons.…”