Enhancement of Responsivity and Sensitivity of p-Silicon/n-Zinc Oxide-Based Photodetector Using Titanium Dioxide Nanoparticles

Monshipouri, Mahta; Molavi, Shahrzad; Mosaddegh, Amirhossein; Sasar, Mahdi; Abdi, Yaser

doi:10.1109/tnano.2020.3022662

Cited by 11 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, a comparison of the performance metrics of ZnO-based photodetectors [48][49][50] has been summarized in Table 1. Therefore, we believe that ZnO NRs coated with TiO2 appear as one of the highly-sensitive self-powered ultraviolet photo detectors.…”

Section: Resultsmentioning

confidence: 99%

Highly sensitive and self powered ultraviolet photo detector based on ZnO nanorods coated with TiO2

Pandey¹,

Shukla

Tripathi³

2023

Sensors and Actuators A: Physical

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Highly sensitive and self powered ultraviolet photo detector based on ZnO nanorods coated with TiO2

Pandey¹,

Shukla

Tripathi³

2023

Sensors and Actuators A: Physical

View full text Add to dashboard Cite

“…For n-type doping of Si, the prepared sample is then placed in a quartz tube furnace at 750 °C, and oxygen gas and argon gas are flown simultaneously into the tube for 30 min to bubble POCl 3 liquid . On the gate oxide of the device, ZnO nanorods are deposited by the hydrothermal method (Figure c). , ZnO nanorods are synthesized by putting samples into an equimolar (7 mM) aqueous solution of zinc nitrate (Zn(NO 3 ) 2 ) and hexamethylenetetramine (HMTA; C 6 H 12 N 4 ). Samples submerged in the solution are kept in an oven at a temperature of 90 °C for 24 h. After 24 h, the samples are dipped in DI water and blow-dried with nitrogen.…”

Section: Experimental Methodsmentioning

confidence: 99%

ZnO Nanorod-Based Solution-Gated Devices for Antibody-Free Rapid Detection of Bacteria

Molavi

Bozorg

Abdi

2021

ACS Appl. Nano Mater.

Self Cite

View full text Add to dashboard Cite

The rapid increase of antibiotic-resistant pathogenic bacteria has become a global health threat. The development of rapid and accurate diagnostics along with the production of drugs is essential to overcome this problem. Previously reported electrochemical, mechanical, and microfluidic sensors have attempted to meet these requirements. However, the fundamental trade-off between the selectivity and sensitivity of such sensors is still the main challenging and unresolved issue. Here, we introduce a labelfree and antibody-free gated device with the capability of sensitive and selective detection of bacteria. Our device with a unique operating mechanism enables us to have a fingerprint for any type of bacteria. Each type of bacteria induces unique electrical characteristics on the device according to its shape and electrostatic properties, which makes it possible to selectively detect the bacteria. Utilizing the ZnO nanorods in the structure of the sensor and using our electrical procedure of detection provide a platform for single-cell detection.

show abstract

“…[7] Despite the significance of photodetectors and the progress made in machine learning (ML) techniques, a research gap exists in the comprehensive ML modeling of photodetector performance prediction. Existing experimental studies have predominantly focused on specific aspects of photodetector optimization, such as material characterization, [8,9] device architecture, [10][11][12] or fabrication process refinement. [13,14] However, there is a noticeable lack of research dedicated to predicting photodetector responsivity using ML models.…”

Section: Introductionmentioning

confidence: 99%

Predicting Photodetector Responsivity through Machine Learning

Arjmandi‐Tash,

Mansourian,

Rahsepar

et al. 2024

Advcd Theory and Sims

Self Cite

View full text Add to dashboard Cite

This study introduces a novel methodology for predicting photodetector responsivity, specifically targeting challenging materials like borophene. The synthesis of these materials faces substantial experimental complexities, necessitating reliable performance predictions before fabrication. To address this, a comprehensive approach leveraging advanced machine learning techniques, specifically artificial neural networks (ANN), is developed. Integration of X‐ray diffraction (XRD) and Raman spectra data into AI models enables efficient prediction of photodetector efficiency prior to device fabrication. The innovation lies in strategically incorporating Generative Adversarial Networks (GANs) for dataset augmentation, significantly expanding the dataset size and enhancing the robustness of the ANN model. Sensitivity analyses highlighted influential factors such as bias voltage and spectral coefficients, validating the approach and aligning with recent experimental findings. This methodology not only advances optoelectronics, but also holds promise for materials science and device engineering. Predictions for Wavelength‐Responsivity plots, considering borophene allotropes as active layers and n‐Si as substrates, show peaks around 300–400 nm, ranging from 0.04 to 0.36 AW−1 at bias voltages between 1 and 5 volts. These estimations assume a borophene layer thickness of approximately 1.6 nm and a radiation power intensity of 5000 µ Wcm−2.

show abstract

Enhancement of Responsivity and Sensitivity of p-Silicon/n-Zinc Oxide-Based Photodetector Using Titanium Dioxide Nanoparticles

Cited by 11 publications

References 41 publications

Highly sensitive and self powered ultraviolet photo detector based on ZnO nanorods coated with TiO2

Highly sensitive and self powered ultraviolet photo detector based on ZnO nanorods coated with TiO2

ZnO Nanorod-Based Solution-Gated Devices for Antibody-Free Rapid Detection of Bacteria

Predicting Photodetector Responsivity through Machine Learning

Contact Info

Product

Resources

About