Ultra-fast AND gate using single semi-reflective quantum dot semiconductor optical amplifier

Mukherjee, Kousik

doi:10.1007/s11107-023-00996-0

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Photon Netw Commun

2023

DOI: 10.1007/s11107-023-00996-0

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Ultra-fast AND gate using single semi-reflective quantum dot semiconductor optical amplifier

Kousik Mukherjee

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2024

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Cited by 3 publications

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Implementation of universal logic gates using 2:1 photonic multiplexer (MUX) of electro-optic Mach–Zehnder interferometer

Das,

Pahari

2024

J Opt

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Implementation of universal logic gates using 2:1 photonic multiplexer (MUX) of electro-optic Mach–Zehnder interferometer

Das,

Pahari

2024

J Opt

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All-optical logic gates based on optimized coherent perfect absorber and fuzzy inference system

Mousavi,

Mansouri-Birjandi,

Rakhshani

et al. 2024

Opt. Continuum

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All-optical logic gates (AO-LGs) are key elements that play a pivotal role in the development of future all-optical computing and all-optical computers. In this paper, benefiting from particle swarm optimization (PSO), an optimized metasurface unit cell in the far-infrared (FIR) frequency band is presented as the basis of four port controlling light with the light system. This system, known as coherent perfect absorption (CPA), could be applied as AO-LGs in certain conditions. NOT, AND, OR, NAND, NOR, XOR, and XNOR logic gates can be implemented with the proposed method. The remarkable innovation of this article is the use of a fuzzy inference system (FIS) instead of a crisp threshold. Different calculated parameters like extinction ratio (ER = 96.2 dB), contrast ratio (CR = 99.54 dB), amplitude modulation (AM = 0.7 dB), and eye-opening (EO = 99%), besides the possibility of utilizing the proposed system for various kind of CPA films, prove the impressive effects of FIS applying as a novelty in this work. Small dimensions and low power consumption are other characteristics of the proposed method that are obtained as a result of using optimized metasurface-based CPA.

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A review of emerging trends in photonic deep learning accelerators

Atwany,

Pardo,

Serunjogi

et al. 2024

Front. Phys.

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Deep learning has revolutionized many sectors of industry and daily life, but as application scale increases, performing training and inference with large models on massive datasets is increasingly unsustainable on existing hardware. Highly parallelized hardware like Graphics Processing Units (GPUs) are now widely used to improve speed over conventional Central Processing Units (CPUs). However, Complementary Metal-oxide Semiconductor (CMOS) devices suffer from fundamental limitations relying on metallic interconnects which impose inherent constraints on bandwidth, latency, and energy efficiency. Indeed, by 2026, the projected global electricity consumption of data centers fueled by CMOS chips is expected to increase by an amount equivalent to the annual usage of an additional European country. Silicon Photonics (SiPh) devices are emerging as a promising energy-efficient CMOS-compatible alternative to electronic deep learning accelerators, using light to compute as well as communicate. In this review, we examine the prospects of photonic computing as an emerging solution for acceleration in deep learning applications. We present an overview of the photonic computing landscape, then focus in detail on SiPh integrated circuit (PIC) accelerators designed for different neural network models and applications deep learning. We categorize different devices based on their use cases and operating principles to assess relative strengths, present open challenges, and identify new directions for further research.

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Ultra-fast AND gate using single semi-reflective quantum dot semiconductor optical amplifier

Cited by 3 publications

References 46 publications

Implementation of universal logic gates using 2:1 photonic multiplexer (MUX) of electro-optic Mach–Zehnder interferometer

Implementation of universal logic gates using 2:1 photonic multiplexer (MUX) of electro-optic Mach–Zehnder interferometer

All-optical logic gates based on optimized coherent perfect absorber and fuzzy inference system

A review of emerging trends in photonic deep learning accelerators

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