An Industry Review: Key Developments Shaping the Plasma Simulation Market

At Quantemol, our goal is to provide the most accurate simulation software and validated chemistry data for the plasma community. Navigating the modern semiconductor landscape requires an intense focus on the physical and chemical realities inside the processing chamber. As fabrication facilities push the limits of atomic-scale manufacturing, traditional modelling approaches have to evolve to keep up.

The semiconductor industry currently faces tough manufacturing hurdles, from the geometric constraints of next-generation nodes to the push for automated, data-driven chamber control. Overcoming these challenges requires a deep integration of advanced reactor physics, predictive modelling, and highly accurate molecular data. This review explores the critical trends reshaping plasma processing today, including the shift toward tailored waveform biasing, the rise of autonomous manufacturing systems, and the move towards high-fidelity digital twins. We will look at how Quantemol’s software ecosystem anchors these advancements in fundamental physics.

Navigating the Sub-3nm Transition and Tailored Waveform Biasing

As semiconductor fabrication facilities push deep into sub-3nm architectures, traditional continuous-wave plasma etching is hitting its physical limits. At this microscopic scale, issues like aspect-ratio distortion and substrate damage become highly pronounced, threatening wafer yield. To combat these geometric limitations, the industry has turned heavily toward Pulsed Capacitively Coupled Plasmas (CCPs) and Tailored Waveform Biasing. This method grants engineers precise control over ion energy distributions, allowing them to manipulate how ions interact with the wafer surface at the atomic level.

This transition highlights the precise commercial relevance of the Quantemol Virtual Tool (QVT). Simulating non-sinusoidal, tailored radiofrequency (RF) waveforms requires highly complex plasma chemistry sets to accurately predict how pulsing affects radical-to-ion ratios at the wafer surface. For the broader plasma simulation market, this shift means that basic empirical models are no longer sufficient. Fabs require advanced multi-physics engines that can handle the transient behavior of pulsed power and non-Maxwellian electron dynamics.

Quantemol’s Chief Technology Officer, Dr. Sebastian Mohr, highlighted how internal software developments are directly addressing these sub-3nm challenges. While the general functionality existed previously, Dr. Mohr noted that the upcoming QVT release makes the setup for multi-frequency and pulsed CCPs significantly easier for the end user. This streamlined workflow allows engineers to seamlessly study the exact effects of pulsing, multi-frequency configurations, and non-sinusoidal waveforms on ion energies and radical-to-ion flux ratios, metrics that are paramount to achieving high-precision etching.

Don’t miss the upcoming QVT release. Contact us today to secure early access updates and claim your free plasma simulation software trial.

The Integration of Data-Driven Systems in 2D Semiconductors

In June 2026, the Korea Institute of Machinery and Materials (KIMM) announced a plasma-based, intelligent integrated system designed specifically for 2D semiconductors. The system uses machine learning to analyse real-time plasma emissions and mass variations, autonomously adjusting synthesis and Atomic Layer Etching (ALE) processes. This represents a significant move toward automated manufacturing where real-world diagnostics and computational models operate together.

For machine learning models to accurately predict process states without endless, expensive physical trial and error, they rely heavily on comprehensive pre-calculated cross-section data and chemistry kinetics. This is the exact underlying data housed in the Quantemol Database (QDB). As autonomous systems become a standard fixture in modern fabs, the demand for verified atomic and molecular data escalates. Quantemol is uniquely positioned to serve as the critical data supplier for these automated control loops, grounding statistical models in fundamental physics.

Reflecting on these automated systems, Quantemol’s Plasma Data Scientist, Dr. Alex Moriarty, provided insight into how the team is currently improving its own machine learning models within QDB to predict missing chemistry data and accelerate reaction set generation. Dr. Moriarty shared that the upcoming QDB website release features an updated diffusion coefficient predictor boasting improved accuracy and the vital capability to distinguish between isomers. Furthermore, internal research pipelines are aimed at utilising machine learning to identify missing reactions within chemistry sets, pioneering a much faster way to generate reduced chemistry sets, and implementing a rapid profiling tool to comprehensively sweep parameter configurations.

High-Fidelity Physics and the Evolution of Digital Twins for Plasma Etching

A major paradigm shift has accelerated, with fabrication plants deploying probabilistic graphical models and simulation-guided algorithms to build operational Digital Twins of etching chambers. These digital twins allow for real-time monitoring, controller fault detection, and rapid process recipe changes without wasting physical wafers. By creating a virtual replica of the reactor, engineers can run predictive scenarios instantly.

However, a digital twin is only as good as its underlying physics engine. This trend underscores the expanding market for tools like QVT, and its underlying foundational codes, such as the Hybrid Plasma Equipment Model (HPEM). Quantemol is positioned to provide the heavy-lifting physics baseline required to construct these high-fidelity digital twins. Our long-term goal is to build an all-encompassing digital twin environment that seamlessly integrates gas-phase chemistry, equipment engineering, and feature-profile evolution.

When evaluating the computational bottlenecks that must be overcome to achieve true real-time execution for such an all-encompassing digital twin, Dr. Mohr pointed out a stark reality regarding current HPEM integration. To achieve the instantaneous execution speeds required by industrial fabs, there is ultimately no other viable path forward than to intelligently blend machine learning algorithms using these core, physics-driven simulations as their foundation.

Computational Physics Refinements in the Latest UKRmol+ Release

The UKRmol+ suite is the foundational engine for calculating the electron-molecule collision data that populates our databases. The latest release brings several significant technical improvements to the R-matrix method, directly enhancing our ability to serve the semiconductor space. A primary update is the implementation of Effective Core Potentials (ECPs), which allows for the streamlined modeling of molecules containing heavier elements. These heavy atoms are increasingly common in modern semiconductor precursors, as well as a range of other diverse applications from fusion plasmas to jet propulsion.

Dr. Greg Armstrong, Quantemol’s Principal Computational Molecular Physicist, highlighted a recent publication where we harnessed this exact capability to calculate cross-section data for WH, a molecule relevant to fusion plasma environments like the ITER facility. The paper also provided cross-section data for SiBr4, a compound used as a precursor in chemical vapor deposition (CVD). These calculations serve as a powerful validation of the technology, proving that ECPs drastically accelerate calculation speeds without compromising rigorous physics.

These updates directly impact Quantemol Electron Collisions (QEC), our user interface for the UKRmol+ codes. The inclusion of ECPs is particularly relevant because it enables the simulation of complex, metal-containing gases with vastly improved computational efficiency by completely bypassing the need to treat all inner-shell electrons explicitly.

Conclusion

The plasma simulation market is moving rapidly from an era of exploratory research into an era of strict, high-precision industrial reliance. As physical experimentation becomes economically unfeasible at the sub-3nm scale, the semiconductor industry relies heavily on virtual engineering. Whether it is through supplying foundational collision data via QDB to feed automated manufacturing loops, using QVT to model complex tailored waveforms, or building the physics baseline for complex digital twins, Quantemol remains central to these industrial advancements. By continually integrating state-of-the-art updates from foundational codes like UKRmol+, we ensure that our users have the precision tools necessary to design the next generation of microelectronics.

Would you like to see how our simulation tools can support your work? Contact us today to discuss your research goals or to arrange a software trial.

By Annie Laver