This work presents an integrated DeNOx approach to enable low‑emission polysilicon etching operations. A key challenge in silicon etching arises from batch‑wise immersion and removal of silicon from the etching solution, which induce 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. In parallel, an enhanced control concept, including Advanced Process Control (APC), was deployed to proactively manage load changes and stabilize catalyst performance under dynamic conditions. The combined measures resulted in a substantial and sustained reduction of 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.

The 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 of scrap and rework.

This talk presents the VisionIQ concept from a manufacturing perspective: how standardized data ingestion, model governance, and production‑ready deployment allow AI vision to scale sustainably in high‑volume semiconductor environments. Real‑world learnings from frontend 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 conception, 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 diminish 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 end of 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 lever for 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 the corporate footprint of Bosch and illustrates the impact at product level using a MEMS device as an example. A comparison of scenarios with and without green electricity highlights the significant reduction potential, 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 achieve a more comprehensive sustainability transformation of semiconductor manufacturing.

Sustainability claims in semiconductors increasingly fail not on intent, but on whether a footprint can be reproduced, compared, and defended under audit. Using EcoInsights workflows, we will show how to generate ISO 14067-aligned PCFs, along with an audit pack that includes assumption registers, sensitivity tests, and traceability artifacts suitable for customer procurement and third-party review.

A deep-dive case study will connect assurance discipline to architectural design decisions in advanced packaging: comparing two die-stack options where a seemingly small choice, such as selecting a 2GB versus 3GB DRAM die, materially shifts the packaged device PCF through die area, yield, and packaging contributions. We will show 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.