In a time when controlling thermal quantities governs the performance and yield of semiconductor devices, Sugirtha Krishnamurthy has created an exciting innovation in thermal modeling for Rajamanickam Gopirajah, Tina Ying Pan, and Syed S.H. Rizvi. Their discovery is a thermal modeling method that has applications far beyond food manufacturing and SCFX. Their work is premised on some key universal principles that provide frameworks for defining and iteratively addressing heat management challenges in semiconductor high-precision processes, which have persisted for so long.
From atomic layer deposition to lithography and wafer bonding, thermal management in semiconductor manufacturing is both mission-critical and increasingly complex. As device geometries shrink and process windows tighten, even minor temperature fluctuations can cause defects, misalignments, and yield loss. Traditional thermal models often fail to predict the dynamic heat behavior in systems involving phase-change materials, reactive gases, or supercritical fluids—all of which are becoming more common in advanced semiconductor fabrication.
Krishnamurthy’s team tackled the challenge of modeling thermal behavior in SCFX—a low-temperature, high-pressure process that shares striking similarities with certain semiconductor techniques like supercritical CO₂ cleaning, supercritical-assisted deposition, and even immersion lithography with non-Newtonian fluids. The model they developed, using Buckingham Pi dimensional analysis, introduces two dimensionless parameters—Dref (geometry-based) and Eref (energy balance-based)—that simplify the heat transfer dynamics into a universal, predictive framework.
In semiconductor terms, these variables could correspond to reactor chamber aspect ratios (Dref) and the ratio of process energy input to thermal dissipation through cooling channels or cryogenic systems (Eref). The capability to model such systems dimensionlessly is very powerful: it allows engineers to predict temperature profiles in new equipment configurations and therefore minimize prototyping iterations while maximizing first-pass success.
While the team validated the model using rice flour and grape pomace in extrusion, the underlying methodology is material-agnostic. What matters is the energy transformation mechanics—how mechanical, electrical, or chemical energy is injected and how it is removed via cooling. This logic mirrors the challenges in thermal interface materials, annealing ovens, and etch chambers, where maintaining thermal stability is essential for uniformity and defect control.
The most compelling crossover is in the model's ability to predict system behavior under scaling conditions. In semiconductor fabrication, where scaling often requires customized equipment across fabs, a validated, dimensionless approach provides a roadmap for deploying new toolsets without relying exclusively on iterative testing.Top-level parameters such as α and β are invariant to the system. K allows specific customization and is an elegant framework for a global fab environment pursuing harmonized process conditions.
Sugirtha Krishnamurthy’s work exemplifies how solutions crafted for one industry can offer high-impact innovations in another. By reframing a food processing problem through the lens of physics and energy flow, she has provided a modeling toolkit with strong potential for semiconductor thermal design, especially in advanced packaging, heterogeneous integration, and 3D stacking, where local thermal hotspots are a known pain point.
As semiconductor processes demand more precision, adaptability, and thermodynamic control, Krishnamurthy’s SCFX-inspired model lays the groundwork for a new class of predictive thermal engineering tools. This convergence of material science, dimensional analysis, and system design could help chipmakers navigate the next wave of fabrication challenges—with insight rather than iteration.