Fig. 1: Gate-all-around inner-spacer analysis with Sentaurus Topography. For Sentaurus Topography, key physics-based differentiators include feature-scale geometry evolution, surface reaction modeling, ion and neutral transport, charging-aware behavior, material-dependent selectivity, and profile prediction across complex 3D structures. Importantly, Sentaurus Topography can be calibrated to experimental data, helping models reproduce new experimental results more accurately. These capabilities make the digital twin more than a data-fitting layer; they provide the physical anchor that allows AI and automation to generalize beyond experiments already performed. Figure 2 broadens this proof point by showing how the same physics foundation applies across high-value feature-scale applications, including deposition, etch, spin-on, CMP, loading effects, ion-induced surface charging, and doping-dependent etching. By connecting process conditions to wafer outcomes through calibrated physical models, Sentaurus Topography helps make digital twin and AI-enabled workflows more trustworthy. 
Fig. 2: Physics-based modeling across critical process applications with Sentaurus Topography. With this foundation, engineers can move beyond empirical iteration to a physics-grounded view of why outcomes occur, which parameters matter, and how the process can be improved. This is also why AI alone is not enough. Empirical data remains constrained by wafer availability, metrology cost, and the practical limits of how many physical experiments can be run. Physics-based simulation can help expand the searchable innovation space by adding mechanistic structure where data is sparse, but its results still require calibration, validation, and expert judgment before they can guide wafer learning cycles. Once physics-based simulation establishes a trusted foundation, the next question is scale: how can engineers create, calibrate, and explore models fast enough to keep up with complexity? Workflow automation helps experts apply that foundation across more mechanisms, recipes, and process conditions than manual methods can support. The next bottleneck: Model creation and exploration As simulation capabilities mature, the bottleneck shifts from execution to model construction: can engineers efficiently define reaction mechanisms, parameter sets, boundary conditions, and calibration strategies that represent the underlying process physics? Creating accurate surface reaction models remains iterative, expert driven, and computationally demanding because engineers must translate incomplete observations into physically plausible mechanisms, tune parameters, and evaluate competing model structures. Experts still spend significant time configuring models, perturbing parameters, running trial simulations, and comparing candidate solutions. The next layer of innovation is workflow automation that reduces repetitive setup while keeping experts focused on physical insight, validation, and judgment. Adding automation to the expert workflow To scale model development, the industry is beginning to automate parts of the modeling workflow while keeping experts responsible for assumptions, validation, and judging whether automated outputs are physically credible. New approaches are emerging in which expert process knowledge is captured in structured model templates, multiple candidate mechanisms are generated automatically, and simulations are evaluated in parallel so engineers can concentrate on physical validation, model selection, and refinement. This shifts the workflow from serial trial and error to guided parallel exploration, allowing engineers to assess multiple model possibilities without manually constructing every candidate. Automation becomes a force multiplier for expertise while keeping engineers responsible for validation and judgment. From simulation to digital twin As simulation and automation evolve, individual tools are giving way to integrated digital twin platforms that make physics-grounded decision-making part of everyday process engineering. A process digital twin brings physics-based simulation, equipment parameters, process knowledge, and manufacturing data into a unified environment usable by process engineers, not only simulation specialists. It lets engineers evaluate virtual process changes, explore what-if scenarios before wafer runs, and connect equipment settings to wafer outcomes without adding manual complexity. The emphasis on physics-grounded digital twin matters because AI capabilities are becoming broadly available. Many entrants can claim faster model generation, automated recipe search, or AI-assisted analysis. What is harder to replicate is validated process physics, feature-scale simulation, calibrated manufacturing context, and workflows that connect virtual predictions back to real wafer behavior. The role of AI, ML, and emerging agentic workflows As these systems evolve further, AI is beginning to play a more prominent role, but its value is strongest when it is grounded in semiconductor physics. ML can accelerate calibration and optimization, AI-driven methods can search high-dimensional parameter spaces and identify latent patterns, and agent-based approaches can help orchestrate complex simulation workflows. The differentiator is not AI by itself; it is AI working on top of validated physics models, process knowledge, and digital twin workflows that reflect how semiconductor manufacturing behaves. Looking ahead, engineers may work with intelligent assistants that configure experiments, suggest model refinements, interpret results, and navigate workflows across models, data, and compute. The goal is not to abstract away physics or remove the engineer, but to put physics knowledge at the center of process engineering and use AI as the interface and acceleration layer. Helping engineers work at the speed of complexity Semiconductor manufacturing complexity is not slowing down. To keep pace, process engineers need workflows that combine physics-based simulation for mechanistic understanding, AI for faster model development, digital twins for decision support, and intelligent assistants to reduce workflow burden. The future of process development lies at the intersection of physics, AI, and digital twin technologies: physics provides the trusted foundation, AI accelerates exploration, and digital twins make that capability usable in everyday workflows. This combination helps process engineers get more value from complex manufacturing equipment while preserving the physics knowledge needed to make virtual process decisions credible. Learn more about Synopsys TCAD here. The post From Feature-Scale Simulation To Digital Twins: Helping Process Engineers Tackle Growing Complexity appeared first on Semiconductor Engineering. Source: https://semiengineering.com/from-featur ... omplexity/