A design of all-optical read-only memory using reflective semiconductor optical amplifier

Bosu, Surajit; Bhattacharjee, Baibaswata

doi:10.1007/s12596-022-00943-8

Cited by 8 publications

(5 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address these challenges within PNNs, various strategies were proposed. These encompass advanced error correction codes customized for optical communication systems, adaptive signal processing algorithms adept at discerning noise from genuine signals, and innovative optical amplifier designs aimed at amplifying signal strength while mitigating noise interference [ 111 , 112 , 113 ]. Furthermore, ongoing research explores techniques such as optical phase modulation and dispersion compensation to counteract signal attenuation and distortion, thereby enhancing the resilience and efficacy of optical systems within neural network frameworks [ 114 , 115 ].…”

Section: Navigating the Landscape Of Pnns: Challenges And Prospectsmentioning

confidence: 99%

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

Khonina,

Kazanskiy,

Skidanov

et al. 2024

Nanomaterials

View full text Add to dashboard Cite

Photonic neural networks (PNNs), utilizing light-based technologies, show immense potential in artificial intelligence (AI) and computing. Compared to traditional electronic neural networks, they offer faster processing speeds, lower energy usage, and improved parallelism. Leveraging light’s properties for information processing could revolutionize diverse applications, including complex calculations and advanced machine learning (ML). Furthermore, these networks could address scalability and efficiency challenges in large-scale AI systems, potentially reshaping the future of computing and AI research. In this comprehensive review, we provide current, cutting-edge insights into diverse types of PNNs crafted for both imaging and computing purposes. Additionally, we delve into the intricate challenges they encounter during implementation, while also illuminating the promising perspectives they introduce to the field.

show abstract

Section: Navigating the Landscape Of Pnns: Challenges And Prospectsmentioning

confidence: 99%

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

Khonina,

Kazanskiy,

Skidanov

et al. 2024

Nanomaterials

View full text Add to dashboard Cite

show abstract

“…Semiconductor Optical Amplifier (SOA) Switches [24][25][26] utilize semiconductor amplifiers to both amplify and switch optical signals, providing versatile functionality. Acousto-Optic Switches [27,28] modify crystal refractive indices through acoustic waves, allowing controlled steering of optical signals.…”

Section: Introductionmentioning

confidence: 99%

A new scheme of all-optical 2:1 multiplexer (MUX) and MUX-based combinational circuits using nonlinear kerr-type materials

Das,

Pahari

2024

Eng. Res. Express

View full text Add to dashboard Cite

Optoelectronics, often known as photon-based electronics, is well recognised for being more effective than conventional electronics for communication. To create a superfast computer, several devices have previously been developed in the field of all optical computing systems. Here, we have put forth an all-optical 2:1 multiplexer with the usage of nonlinear material exhibiting a strong ac Kerr effect and 2:1 Multiplexer (MUX) based all optical -photonic switches - NOT, OR, AND, NAND, NOR etc. The sole method for building combinational logic circuits is to utilise MUX. This work is made simpler by the Shannon decomposition theorem than by other approaches. In this study, input and output are expressed as the presence of a light signal with a prefixed intensity as binary 1 and the absence of a light signal as binary 0 using an intensity-based all-optical device and positive logic. Since the system is entirely optical, our current design enables very-high speed (terahertz) computation, which is not possible with typical semiconductor electronic switches. Validation through mathematical computations-based PYTHON simulations establishes the efficacy of the proposed approach. This research holds promise for applications in industrial and commercial contexts, offering enhanced performance and versatility.

show abstract

“…[2][3][4][5][6][7][8][9] In last few years, some researchers have been proposed TOAD, RSOA, ring resonator-based memory devices. [10][11][12][13][14][15][16][17] Maity et al 10 proposed TOAD-based binary flip flop with lower values of extinction ratio (ER) (13.18 dB) and contrast ratio (CR) (18.023 dB). They used very high control pulse energy (200fJ).…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have already designed all‐optical devices using different mechanism or optical switches such as SOA, MZI‐SOA, RSOA, terahertz optical asymmetric demultiplexer (TOAD), photonic crystal, ring resonator, and so on 2–9 . In last few years, some researchers have been proposed TOAD, RSOA, ring resonator‐based memory devices 10–17 . Maity et al 10 proposed TOAD‐based binary flip flop with lower values of extinction ratio (ER) (13.18 dB) and contrast ratio (CR) (18.023 dB).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

All‐optical Tera Hertz Optical Asymmetric Demultiplexer‐based Memory at a data rate of 300 Gbps

Maji,

Mukherjee,

Mandal

2024

Micro & Optical Tech Letters

View full text Add to dashboard Cite

All‐optical memory units play an important role in the operation of an optical computer. In these communications, we have designed all‐optical single‐bit memory unit or latch using Tera Hertz Optical Asymmetric Demultiplexer. The performance of single‐bit memory unit is evaluated and numerically simulated by MATLAB. We have numerically calculated the output extinction ratio (35.57 dB), contrast ratio (36.61 dB), and Q value (40.34) to analyze the memory or latch circuit. High values of extinction ratio, contrast ratio, and Q confirm the feasibility of the single‐bit memory unit.

show abstract

A design of all-optical read-only memory using reflective semiconductor optical amplifier

Cited by 8 publications

References 33 publications

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

A new scheme of all-optical 2:1 multiplexer (MUX) and MUX-based combinational circuits using nonlinear kerr-type materials

All‐optical Tera Hertz Optical Asymmetric Demultiplexer‐based Memory at a data rate of 300 Gbps

Contact Info

Product

Resources

About