ArticleScientific reports2025
Optical XOR logic gate design in two dimensional photonic crystal using ANN and PSO.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- ANN-PSO Hybrid ML-Optimization of a Hollow-Disk Resonator-Based Photonic Crystal Optical Sensor for HeLa Cell Tumor Detection.Sensors (Basel, Switzerland) · 2026Article
- Enhanced design of a photonic crystal biosensor for early cervical cancer detection using refractive index variation and neural network classification.Scientific reports · 2026Article
- Design and analysis of a GaN-based 2D photonic crystal biosensor integrated with machine learning techniques for detection of skin diseases.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we present an all-optical XOR gate based on two-dimensional photonic crystals (PCs). The proposed gate is designed to be used in optical computing and all-optical logic applications, offering advantages such as fast computation and parallel processing. To enhance the design process and optimize its performance, we employ artificial neural network (ANN) to model the gate's behavior. A dataset of 555 samples, obtained from the Finite-Difference Time-Domain (FDTD) method, is used to train the ANN model. Subsequently, particle swarm optimization (PSO) is used to find optimal values of XOR design parameters, including the lattice constant and the radius of the rods. This optimization process leads to an optimized structure that enhances the gate's performance. The proposed all-optical XOR gate exhibits a range of output powers across the respective logical states (desired state): 0 (0), 0.995 (1), 0.935 (1), and 0.015 (0). The proposed structure achieved an acceptable contrast ratio of 17.947 dB. By leveraging the ANN-PSO approach to improve the performance of PCs, we demonstrate a promising strategy for developing complex and efficient optical computing systems.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.