Dataset Information

Surface plasmon enhanced Organic color image sensor with Ag nanoparticles coated with silicon oxynitride.

ABSTRACT: As organic photodetectors with less than 1 ?m pixel size are in demand, a new way of enhancing the sensitivity of the photodetectors is required to compensate for its degradation due to the reduction in pixel size. Here, we used Ag nanoparticles coated with SiO_xN_y as a light-absorbing layer to realize the scale-down of the pixel size without the loss of sensitivity. The surface plasmon resonance appeared at the interface between Ag nanoparticles and SiO_xN_y. The plasmon resonance endowed the organic photodetector with boosted photon absorption and external quantum efficiency. As the Ag nanoparticles with SiO_xN_y are easily deposited on ITO/SiO₂, it can be adapted into various organic color image sensors. The plasmon-supported organic photodetector is a promising solution for realizing color image sensors with high resolution below 1 ?m.

SUBMITTER: Heo S

PROVIDER: S-EPMC6959276 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Surface plasmon enhanced Organic color image sensor with Ag nanoparticles coated with silicon oxynitride.

Heo Sung S Lee Jooho J Lee Gae Hwang GH Heo Chul-Joon CJ Kim Seong Heon SH Yun Dong-Jin DJ Park Jong-Bong JB Kim Kihong K Kim Yongsung Y Lee Dongwook D Park Gyeong-Su GS Cho Hoon Young HY Shin Taeho T Yun Sung Young SY Kim Sunghan S Jin Yong Wan YW Park Kyung-Bae KB

Scientific reports 20200114 1

As organic photodetectors with less than 1 μm pixel size are in demand, a new way of enhancing the sensitivity of the photodetectors is required to compensate for its degradation due to the reduction in pixel size. Here, we used Ag nanoparticles coated with SiOxNy as a light-absorbing layer to realize the scale-down of the pixel size without the loss of sensitivity. The surface plasmon resonance appeared at the interface between Ag nanoparticles and SiOxNy</ ...[more]

PMID: 31937814

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Project description:Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, efficient collection before thermalization remains an obstacle for realization of their full energy generating potential. Addressing this challenge requires detailed understanding of physical processes from plasmon excitation in the metal to their collection in a molecule or a semiconductor, where atomistic theoretical investigation may be particularly beneficial. Unfortunately, first-principles theoretical modeling of these processes is extremely costly, preventing a detailed analysis over a large number of potential nanostructures and limiting the analysis to systems with a few 100s of atoms. Recent advances in machine learned interatomic potentials suggest that dynamics can be accelerated with surrogate models which replace the full solution of the Schrödinger Equation. Here, we modify an existing neural network, Hierarchically Interacting Particle Neural Network (HIP-NN), to predict plasmon dynamics in Ag nanoparticles. The model takes as a minimum as three time steps of the reference real-time time-dependent density functional theory (rt-TDDFT) calculated charges as history and predicts trajectories for 5 fs in great agreement with the reference simulation. Further, we show that a multistep training approach in which the loss function includes errors from future time-step predictions can stabilize the model predictions for the entire simulated trajectory (∼25 fs). This extends the model's capability to accurately predict plasmon dynamics in large nanoparticles of up to 561 atoms, not present in the training data set. More importantly, with machine learning models on GPUs, we gain a speed-up factor of ∼103 as compared with the rt-TDDFT calculations when predicting important physical quantities such as dynamic dipole moments in Ag55 and a factor of ∼104 for extended nanoparticles that are 10 times larger. This underscores the promise of future machine learning accelerated electron/nuclear dynamics simulations for understanding fundamental properties of plasmon-driven hot carrier devices.

Dataset Information

Surface plasmon enhanced Organic color image sensor with Ag nanoparticles coated with silicon oxynitride.

Publications

Surface plasmon enhanced Organic color image sensor with Ag nanoparticles coated with silicon oxynitride.

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