Igor D. Kaganovich

Plasma cathodes often exhibit a bi-modal current–voltage behavior whose physical origin remains unclear, where a low voltage, low current regime transitions to a higher current, high voltage regime at some transition point. Here, two-dimensional particle-in-cell simulations using EDIPIC-2D are used to study an argon electron-cyclotron-resonance (ECR) plasma cathode with an extracting anode, with the goal of identifying the kinetic mechanism connecting this transition point to the formation of a confined orifice plasma. The simulations reveal two self-consistent regimes: a low-current, diffusion dominated state in which the supply of electrons to the orifice is limited, and a high-current state characterized by a dense orifice plasma separated from the prescribed discharge plasma by a weak electrostatic double layer. In the latter regime, the double layer sustains intense localized ionization by accelerating source electrons while regulating particle exchange across the interface. Ion-acoustic wave activity is observed, and a low–Mach-number ion acoustic shock forms within the double-layer region that produces a pronounced reversal in axial ion current density immediately below this interface. Time-resolved diagnostics reveal the formation mechanism for this orifice plasma that culminates in the coupling of the source, orifice, and plume regions. These results reveal a kinetic, time-dependent mechanism for the bi-modal transition in plasma cathodes and identify electron supply, plume-ionization seeding, and double-layer–shock dynamics as key elements governing access to the high-current regime.

Two-dimensional particle-in-cell simulations of an inductively coupled plasma (ICP) are used to investigate the influence of radio-frequency (RF) current waveform and frequency on plasma characteristics, collision processes, and the electron velocity distribution function (EVDF). Plasmas driven by sinusoidal coil current over the frequency range of 1–30 MHz and by asymmetric triangular waveform current at 1 MHz are examined at 25 mTorr. For sinusoidal excitation, significantly lower coil current is required at higher frequencies to achieve comparable electron density, consistent with the scaling of inductive electric fields with frequency. Plasma production shifts toward the plasma—dielectric interface with increasing frequency due to reduced skin depth. Despite differences in power deposition localization, strong ambipolar diffusion in the small chamber results in broadly similar plasma density profiles across frequencies. For asymmetric triangular waveform current, the inductive electric field is strongly enhanced during the faster current ramp-down phase, leading to pronounced temporal modulation of electron heating. Increasing waveform duty cycle (DC) enhances the contribution of higher current harmonics, which results in higher steady-state electron density. Excitation and ionization processes also shift closer to the plasma—dielectric interface. The EVDF becomes increasingly asymmetric with increasing DC, reflecting preferential electron acceleration during the current ramp-down phase. Time-resolved analysis shows that excitation and ionization rates peak sharply during this phase, with ionization exhibiting stronger relative enhancement. The intense plasma current produced during the current ramp-down phase persists beyond the reversal of the source electric field due to electron inertia and collisional effects. Consequently, inductive electron power deposition continues in the plasma for several tens of nanoseconds after the coil current reverses direction. These results demonstrate that RF current waveform asymmetry provides a viable mechanism for controlling electron power deposition, EVDF, and electron-impact collisional processes in ICPs.

Laser-driven proton acceleration provides a powerful route for generating ultrashort, high-charge proton beams. Many applications, including secondary neutron sources and inertial fusion, benefit from tight proton beam focusing. Concave targets offer a robust solution, yet the scaling of proton focusing with laser and target parameters remains poorly understood. Here, we present a numerical study of laser-driven proton acceleration and focusing from hemispherical targets using the fully kinetic, relativistic Particle-In-Cell code EPOCH. We focus on the subpicosecond laser-pulse regime (duration ≲ 10 2 fs), centering on the laser parameters of our recent experiment at the Colorado State University ALEPH laser facility. We investigate the proton acceleration mechanisms, characterize proton focusing, and assess how focal spot parameters scale with laser and target parameters. We identify Target Normal Sheath Acceleration as the dominant mechanism, supplemented by a secondary postacceleration stage near the geometrical center of the hemisphere. We demonstrate that both the proton focal spot size and focal plane position scale approximately linearly with the hemisphere radius, with the focal plane consistently located downstream of the geometrical center. The opening angle of the concave target mainly affects the proton beam waist. Energy-dependent proton focusing can be understood, in part, as arising from the evolving curvature of the accelerating structure, which departs from the target curvature. Evidence for self-similar proton focusing is found in the regime of nearly uniform target irradiation.

Atomic-scale control in plasma processing is becoming increasingly critical for fabricating of advanced semiconductor devices, particularly as the industry shifts toward three-dimensional (3D) architectures and high-aspect-ratio (HAR) structures. This review presents a comprehensive overview of recent developments in atomic-scale controlled plasma processes, organized along two key directions: the hierarchical structure of plasma–surface interactions and the generational evolution of atomic layer processing (ALP) technologies. We examined the gas phase, where molecular design enables selective generation of ions and radicals; the boundary layer, where transport phenomena govern species delivery into nanoscale features, and the surface, where temperature-dependent reactions and cyclic processing determine etching selectivity and precision. Building on this foundation, we outline five generations of ALP—from thermal atomic layer deposition to transport-aware, temporally and structurally decoupled processes—highlighting the increasing sophistication of process control. The review further explores the transition from empirical recipe development to science-based, data-driven methodologies. By integrating quantum-chemical modeling, advanced diagnostics, and machine learning, we demonstrated how predictive models can link plasma species composition to process outcomes, enabling autonomous and adaptive control strategies. Finally, this review discusses the broader societal implications of plasma process innovation through the E ⁴ quartet: energy and resource efficiency, environmental sustainability, evolutionary advancement, and educational promotion. These principles guide the development of sustainable and intelligent atomic-scale manufacturing technologies that are not only technically advanced but also socially responsible.

Low-temperature plasma processing is a promising technique for tailoring transition metal dichalcogenides (TMDs). For chalcogen substitution processing, a key challenge is to identify the ion energy window that enables selective chalcogen removal while preserving the metal lattice. Using ab initio molecular dynamics (AIMD), we demonstrate that oxygen and fluorine functionalization widen the processing window by significantly lowering the sulfur sputtering energy threshold (E_sputt,S) of MoS₂ from ∼30 to ∼10 eV via formation of sputtering products such as SO₂ and SF_n. Additionally, we show that experimentally relevant cryogenic temperatures strongly affect E_sputt,S(T) . The dependence is confirmed via AIMD and also predicted by a mechanistic parameter-free theory, suggesting that E_sputt (T) generalizes to other TMDs, functionalizations, and surface impact conditions. Our results highlight oxygen/fluorine functionalization, ionic impact angle, and material temperature to be key control parameters for selective, damage-controlled chalcogen removal in TMD processing.

We introduce a scale-aware, data-driven deep learning modeling framework for accurately predicting the time evolution of multi-scale turbulent plasma and liquid flows. The approach is motivated by the idea of scale separation. Structures of vastly different length scales emerge in these systems, and interactions between these structures occur only locally. To exploit this structure, the flow state is transformed by a hierarchical, fully convolutional autoencoder, not into a single embedding layer as in conventional convolutional surrogate models, but into a series of embedding layers. A stepwise training strategy ensures that fine-scale features are encoded on a high-resolution grid, while larger structures are represented on progressively coarser layers. The time evolution predictor advances all embedding layers in sync, capturing local interactions between features at the same scale as well as between all scales. This approach enables efficient modeling of multi-scale systems since negligible interactions between distant, small-scale structures do not need to be directly modeled. Our hierarchical-embedding autoencoder with a predictor framework is evaluated on canonical examples of multi-scale turbulence: two-dimensional Kolmogorov flow and Hasegawa–Wakatani plasma turbulence. In both cases, the proposed framework significantly improves predictive accuracy relative to conventional convolutional network architectures. A significant improvement in prediction accuracy was observed for crucial statistical characteristics of the Hasegawa–Wakatani plasma as well as for individual trajectories of the Kolmogorov flow turbulence. Importantly, the model's rollout for the Hasegawa–Wakatani problem demonstrates a four-order-of-magnitude speedup compared to traditional numerical solvers.

Partially ionized plasma physics has attracted increased attention recently due to numerous technological applications made possible by the increased sophistication of computer modelling, the depth of the theoretical analysis, and the technological applications to a vast field of manufacturing for computer components. Partially ionized plasma is characterized by a significant presence of neutral particles in contrast to the fully ionized plasma. The theoretical analysis is based upon solutions of the kinetic Boltzmann equation, yielding the non-Maxwellian electron energy distribution function (EEDF), thereby emphasizing the difference with a fully ionized plasma. The impact of the effect on discharges in inert and molecular gases is described in detail, yielding the complex nonlinear phenomena resulting in plasma selforganization. A few examples of such phenomena are given, including the non-monotonic EEDFs in the discharge afterglow in a mixture of argon with the molecular gas NF₃; the explosive generation of cold electron populations in capacitive discharges, hysteresis of EEDF in inductively coupled plasmas. Recently, highly advanced computer codes were developed in order to address the outstanding challenges in plasma technology. These developments are briefly described in general terms.

The following individuals served as reviewers for manuscripts submitted to and/or published in Physics of Plasmas during 2025. The Editor, Associate Editors, an