Digital Twins in Semiconductor Fabrication: Erik Hosler on Cutting Waste Through Virtual Precision
Semiconductor manufacturing is an industry defined by complexity, precision, and relentless pressure to innovate. Yet behind every technological leap lies an uncomfortable reality, including elevated levels of waste generated by trial-and-error approaches to fabrication. As processes shrink to the nanoscale, the margin for error becomes vanishingly small, and mistakes come with massive costs in both materials and energy. Erik Hosler, a specialist in precision manufacturing, recognizes that the industry’s future depends on adopting more innovative systems capable of predicting and preventing defects rather than reacting to them. His perspective underscores why digital twin technology is emerging as a transformative solution for waste reduction in semiconductor fabrication.
Digital twins, such as virtual replicas of fabs, processes, and equipment, allow manufacturers to simulate outcomes before making physical changes. These digital models integrate real-time data from sensors and production lines to create a mirror image of the fab environment, enabling predictive adjustments that minimize costly errors. By reducing the need for repeated physical testing, digital twins help fabs save resources, improve yields, and move toward sustainability goals. Understanding how they function and the challenges they address reveals their growing importance in the semiconductor industry.
The Trial-and-Error Problem in Chipmaking
Semiconductor fabrication involves hundreds of intricate steps, from wafer preparation to lithography, etching, and packaging. Each step must be executed with nanometer-scale precision. Historically, many improvements in these processes have come through trial-and-error experimentation. While this approach has led to innovation, it also generates significant waste, wafers that fail inspection, materials that cannot be reused, and energy consumed in failed production runs.
As devices become smaller and designs more complex, the costs of this waste rise sharply. A single defective wafer can represent thousands of dollars in lost value. Multiply across an entire batch, and the stakes for minimizing error become clear. The industry increasingly needs tools that can anticipate problems before they occur, and this is where digital twins excel.
What Are Digital Twins?
A digital twin is a virtual model that replicates the physical characteristics and behaviors of an object, system, or process. In semiconductor fabs, it means creating digital counterparts of manufacturing equipment, cleanroom environments, or even entire process flows.
These models are powered by data: sensors embedded in equipment feed real-time information into the twin, which then simulates outcomes under varying conditions. For example, a digital twin might predict how adjusting the temperature in a deposition chamber will affect film thickness or how changing the alignment of a lithography system could impact defect rates. Instead of running costly experiments on physical wafers, engineers test scenarios virtually, minimizing risk and waste.
Preventing Defects Before They Happen
One of the most significant advantages of digital twins is their ability to detect potential problems before they manifest in physical production. By running simulations, engineers can identify the conditions that are most likely to cause defects and adjust processes accordingly.
It is particularly critical at the nanoscale, where even the slightest variation in pressure, light exposure, or chemical concentration can ruin an entire wafer. Digital twins make it possible to fine-tune these parameters with precision, reducing the incidence of errors and improving yield rates. The shift from reactive to predictive defect management transforms efficiency and sustainability in fabs.
Expert Perspective: Precision and Yield
The push toward precision in semiconductor manufacturing is not just about better performance, but also about sustainability and efficiency. The less waste is generated, the fewer materials and resources are consumed, and the smaller the environmental footprint of chipmaking is.
Erik Hosler emphasizes, “The ability to detect and measure nanoscale defects with such precision will reshape semiconductor manufacturing. These technologies can enable higher yields, improved quality control, and faster ramp to yield, which in turn reduces costs.” His insight connects directly to the role of digital twins, which function as these very technologies. By providing unprecedented visibility into process variations, digital twins help fabs not only detect defects but also prevent them, ensuring fewer wasted wafers and more efficient use of resources.
This perspective highlights why precision and sustainability are inseparable goals. Reducing waste in fabs is not just about saving money but also about building a manufacturing model that is resilient, efficient, and environmentally responsible.
Improving Sustainability Metrics
Semiconductor manufacturing is under increasing scrutiny for its environmental impact, from high water usage to massive energy demands. Digital twins provide a pathway to more sustainable operations by reducing trial-and-error processes that consume resources and time.
Every defective wafer scrapped represents wasted silicon, chemicals, and hours of machine energy. By improving yield rates, digital twins reduce the total resource burden required to produce working chips. Moreover, they shorten ramp-to-yield timelines, which means fabs can stabilize production faster and with fewer missteps. These improvements translate into measurable gains in key sustainability metrics such as waste reduction, energy efficiency, and carbon footprint.
Challenges in Adoption
Despite their promise, digital twins are not a plug-and-play solution. Creating accurate models requires enormous volumes of high-quality data, and not all fabs are equipped to generate or manage this data effectively. Integrating legacy systems into digital twin frameworks can also be costly and complex.
The sophistication of these models demands skilled personnel who can interpret results and make informed adjustments. Without the right expertise, digital twins risk becoming underutilized tools rather than transformative systems. Overcoming these challenges will be crucial for unlocking their full potential in waste reduction.
A Virtual Path to a More Sustainable Future
Digital twins are redefining semiconductor manufacturing by shifting the industry away from wasteful trial-and-error practices and toward predictive, data-driven precision. By modeling processes virtually, they prevent defects, reduce resource consumption, and accelerate ramp-to-yield, all critical for improving sustainability in a sector known for its heavy environmental footprint.
Efficiency and sustainability are not competing priorities but complementary goals, achievable through more intelligent systems and sharper insights. As digital twins become more sophisticated, powered by AI and real-time data, they will stand at the center of a new manufacturing paradigm, one where innovation and responsibility move forward together. For the semiconductor industry, it is more than a technological upgrade; it is a necessary development toward a sustainable future.