This work presents an integrated DeNOx approach to enable low-emission polysilicon etching operations. A key challenge in silicon etching arises from the batch-wise immersion and removal of silicon from the etching solution, which result in highly dynamic process conditions and sharp NOx concentration gradients in the exhaust gas. These transients place significant demands on downstream abatement systems. To address this, advanced real-time NOx measurements were implemented upstream of the DeNOx catalyst, enabling fast response times and high-resolution monitoring of rapidly changing emission profiles. Catalyst operation was further optimized through improved gas mixing, ensuring uniform flow distribution and full utilization of the active catalyst surface. At the same time, an enhanced control concept, including Advanced Process Control (APC), was deployed to proactively manage load changes and stabilize catalyst performance under dynamic conditions. These combined measures resulted in a substantial and sustained reduction in NOx emissions while increasing etching productivity, demonstrating a scalable pathway toward environmentally optimized polysilicon manufacturing.

Semiconductor manufacturing faces increasing pressure to improve yield, reduce scrap, and lower its environmental footprint—while managing growing process complexity and data volumes. VisionIQ addresses this challenge by introducing a scalable, AI-driven vision framework that transforms image-based inspection data into actionable production insights.

This approach moves beyond isolated, rule-based vision solutions toward a unified architecture for AI image classification across multiple process steps and equipment types. By detecting subtle defect patterns earlier and more consistently, VisionIQ enables faster root-cause analysis, targeted process adjustments, and a measurable reduction in scrap and rework.

This talk presents the VisionIQ concept from a manufacturing perspective: how standardized data ingestion, model governance, and production-ready deployment enable AI vision to scale sustainably in high-volume semiconductor environments. Real-world insights from front-end manufacturing illustrate how VisionIQ contributes not only to yield improvement and cost savings, but also to resource efficiency and long-term sustainability in semiconductor production.

Integrated sustainability embeds environmental and social criteria into every step of the value chain—from technology and product design, through manufacturing and use, to reuse and recycling—while systematically tracking economic impact. By designing products for energy efficiency, durability, repairability, and recyclability, companies reduce material and energy consumption, limit waste, and mitigate regulatory and reputational risks. In manufacturing, resource optimization, process efficiency, and responsible sourcing translate directly into lower operating costs and more resilient supply chains. At the end of a product’s life, strategies such as remanufacturing, component harvesting, and closed-loop recycling recover material and functional value that would otherwise be lost. When these actions are coordinated within a circular business model, the cumulative effect is clear: over the full lifecycle, sustainable design and operations generate more gains than losses and more income than spending—through cost savings, new revenue streams, risk reduction, and enhanced brand and market positioning.

The increasing use of green electricity is a key factor in reducing the carbon footprint of semiconductor manufacturing, but it does not eliminate all relevant environmental impacts. This paper analyzes the effect of green electricity on Bosch’s corporate footprint and illustrates the impact at the product level using a MEMS device as an example. A comparison of scenarios with and without green electricity highlights the significant potential for reduction, while also revealing remaining contributors such as process gases, abatement efficiency, and consumables.

Going beyond climate change mitigation, the paper extends the perspective to biodiversity impacts, focusing on material origin and supply chains for critical raw materials such as gold and platinum group metals. The analysis shows that further progress requires increased use of recycled precious metals, improved abatement technologies, and the development of alternative process gases. These measures are essential to achieving a more comprehensive sustainability transformation of semiconductor manufacturing.

Sustainability claims in the semiconductor industry are increasingly being challenged not because of intent, but because of whether a carbon footprint can be reproduced, compared, and defended during an audit. Using EcoInsights workflows, we will demonstrate how to generate ISO 14067-compliant PCFs, along with an audit package that includes assumption registers, sensitivity tests, and traceability artifacts suitable for customer procurement and third-party review.

A deep-dive case study will link assurance practices to architectural design decisions in advanced packaging: comparing two die-stack options where a seemingly minor choice—such as selecting a 2GB versus a 3GB DRAM die—significantly alters the packaged device’s PCF through die area, yield, and packaging contributions. We will demonstrate how boundary and allocation choices can exaggerate or mask that design impact, and how standardization and assurance controls restore decision-grade comparability, enabling engineering and procurement teams to make defensible “twin transformation” trade-offs.