Electron–phonon relaxation at the Au/WSe<sub>2</sub> interface is significantly accelerated by a Ti adhesion layer: time-domain <i>ab initio</i> analysis

Lu, Teng-Fei; Gumber, Shriya; Tokina, Marina V.; Tomko, John A.; Hopkins, Patrick E.; Prezhdo, Oleg V.

doi:10.1039/d2nr00728b

Cited by 9 publications

(7 citation statements)

References 83 publications

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“…The p-terminal current (20 nA) is slightly higher than the n-terminal current (17 nA) in Figure S2a. A previous study demonstrated that the ambipolar behavior can be explained based on Fermi-level shifting under the electric field because of the weak Fermi-level pinning at the metal/WSe 2 interface. − The balanced electron and hole transport characteristic results from identical n- and p-type Schottky barriers …”

Section: Resultsmentioning

confidence: 99%

Memory Window Ratio Enhancement of p-Type WSe₂ Memtransistors Using Dielectric GeSe₂ Nanosheets with Asymmetric Interfaces for Neuromorphic Computing

Wang,

Lv,

et al. 2023

ACS Appl. Nano Mater.

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The emergence of 2D wide-bandgap semiconductor (WBGS) GeSe 2 as charge-trapping dielectrics has helped realize superior devices by using an extremely simple device setup. However, the controllability of deep-gap-state defects in 2D GeSe 2 poses a challenge due to the vulnerability and susceptibility of charge-trapping centers, resulting in various problems, i.e., small memory window and poor device durability during programming. Herein, we deliberately perform asymmetric interfacial oxidation to reinforce the memory performance based on the WSe 2 /Janus-GeSe 2 van der Waals heterostructure, which exhibits a giant memory window ratio of 88.5% (70.8 V at ±40 V gate sweep range), high-room-temperature hole mobility of 15 cm 2 V −1 s −1 , and low nonlinearity factor close to 0 with regard to synaptic weight update characteristics. The information storage performance is excellent, owing to the interface rendered by asymmetric oxidation in the GeSe 2 layer, providing an effective charge-trapping layer and atomically flat surface to enhance the mobility of the WSe 2 channel. The controllable strategy helps to derive a simple design principle to realize high-performance 2D WBGS GeSe 2 -based memory and electrical synaptic devices with complex neural functions.

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Section: Resultsmentioning

confidence: 99%

Memory Window Ratio Enhancement of p-Type WSe₂ Memtransistors Using Dielectric GeSe₂ Nanosheets with Asymmetric Interfaces for Neuromorphic Computing

Wang,

Lv,

et al. 2023

ACS Appl. Nano Mater.

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“…The NA-MD simulations were performed using the decoherence-induced surface hopping (DISH) technique implemented in TD-DFT based on the Kohn–Sham (KS) framework. , Decoherence is the destruction of the superposition state formed by a pair of electronic states in the system via NAC. , The decoherence time can be estimated as the pure-dephasing time in optical response theory . In the DISH algorithm, quantum jumps occur as a result of the decoherence process, which provides the physical basis for the jumps. ,, This method has been applied to study the photoexcitation dynamics of various materials, including metal oxides, − low-dimensional materials, − perovskites, ,,,,− and other systems. , …”

Section: Methodsmentioning

confidence: 99%

Nuclear Quantum Effects Prolong Charge Carrier Lifetimes in Hybrid Organic–Inorganic Perovskites

et al. 2023

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Hybrid organic–inorganic perovskites (HOIPs) contain light hydrogen atoms that exhibit significant nuclear quantum effects (NQEs). We demonstrate that NQEs have a strong effect on HOIP geometry and electron–vibrational dynamics at both low and ambient temperatures, even though charges in HOIPs reside on heavy elements. By combining ring-polymer molecular dynamics (MD) and ab initio MD with nonadiabatic MD and time-dependent density functional theory and focusing on the most studied tetragonal CH3NH3PbI3, we show that NQEs increase the disorder and thermal fluctuations through coupling of the light inorganic cations to the heavy inorganic lattice. The additional disorder induces charge localization and decreases electron–hole interactions. As a result, the nonradiative carrier lifetimes are extended by a factor of 3 at 160 K and 1/3 at 330 K. The radiative lifetimes are increased by 40% at both temperatures. The fundamental band gap decreases by 0.10 and 0.03 eV at 160 and 330 K, respectively. By enhancing atomic motions and introducing new vibrational modes, NQEs strengthen electron–vibrational interactions. Decoherence, determined by elastic scattering, accelerates almost by a factor of 2 due to NQEs. However, the nonadiabatic coupling, driving nonradiative electron–hole recombination, decreases because it is more sensitive to structural distortions than atomic motions in HOIPs. This study demonstrates, for the first time, that NQEs should be considered to achieve an accurate understanding of geometry evolution and charge carrier dynamics in HOIPs and provides important fundamental insights for the design of HOIPs and related materials for optoelectronic applications.

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“…We also investigate the dynamics of hot carriers by tracking electron and hole differences separately. Most theoretical studies use an energy-space analysis for plasmonic hot carriers, for example, hot carrier distributions and scattering lifetimes as functions of their energy, or evolving these hot carrier energies and densities over time. ,,, In this work, we use a simplified version of this analysis, where changes in occupation above (below) the Fermi level are assigned as electrons (holes), resulting in gross atomic assignment of negative (positive) charges on each atom. We get the transition probability of electron from an occupied Kohn–Sham state o to an unoccupied Kohn–Sham state u , creating a hole, from the change in density matrix, P o u ( t ) = | δ ρ o u false( t false) f o − f u | 2 where f o ( u ) is the occupation number associated with the electronic states of o ( u ) below (above) the Fermi energy level.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles

et al. 2023

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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.

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Electron–phonon relaxation at the Au/WSe₂ interface is significantly accelerated by a Ti adhesion layer: time-domain ab initio analysis

Abstract: On introduction of a thin Ti adhesion layer at the Au/WSe2 interface, the electron–phonon coupling strengthens which results in accelerated excited charge carrier relaxation.

Cited by 9 publications

References 83 publications

Memory Window Ratio Enhancement of p-Type WSe₂ Memtransistors Using Dielectric GeSe₂ Nanosheets with Asymmetric Interfaces for Neuromorphic Computing

Memory Window Ratio Enhancement of p-Type WSe₂ Memtransistors Using Dielectric GeSe₂ Nanosheets with Asymmetric Interfaces for Neuromorphic Computing

Nuclear Quantum Effects Prolong Charge Carrier Lifetimes in Hybrid Organic–Inorganic Perovskites

Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles

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Electron–phonon relaxation at the Au/WSe2 interface is significantly accelerated by a Ti adhesion layer: time-domain ab initio analysis

Abstract: On introduction of a thin Ti adhesion layer at the Au/WSe2 interface, the electron–phonon coupling strengthens which results in accelerated excited charge carrier relaxation.

Cited by 9 publications

References 83 publications

Memory Window Ratio Enhancement of p-Type WSe2 Memtransistors Using Dielectric GeSe2 Nanosheets with Asymmetric Interfaces for Neuromorphic Computing

Memory Window Ratio Enhancement of p-Type WSe2 Memtransistors Using Dielectric GeSe2 Nanosheets with Asymmetric Interfaces for Neuromorphic Computing

Nuclear Quantum Effects Prolong Charge Carrier Lifetimes in Hybrid Organic–Inorganic Perovskites

Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles

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Electron–phonon relaxation at the Au/WSe₂ interface is significantly accelerated by a Ti adhesion layer: time-domain ab initio analysis

Memory Window Ratio Enhancement of p-Type WSe₂ Memtransistors Using Dielectric GeSe₂ Nanosheets with Asymmetric Interfaces for Neuromorphic Computing

Memory Window Ratio Enhancement of p-Type WSe₂ Memtransistors Using Dielectric GeSe₂ Nanosheets with Asymmetric Interfaces for Neuromorphic Computing