AI & ML

IMPROVING YIELD AND QUALITY WITH AI SOLUTIONS

In the late 2000s, Elisa made a significant impact by exploiting the potential of AI and machine learning to enhance its telecommunication operations, with a special emphasis on automation and predictive maintenance. Today, Elisa is applying this acquired knowledge to various other industries. To achieve this, Elisa has been integrating its AI and ML expertise into the solutions offered by its subsidiary companies that specialise in industrial software.

In collaboration with our subsidiaries, we have been creating AI-boosted solutions to improve yield and quality for a diverse set of manufacturing organisations, such as the semiconductor industry, battery manufacturing, electronics, and more. Discover more about our use cases that demonstrate how our innovative, data-centric strategy can enhance competitiveness and foster sustainable growth.

IMPROVING YIELD AND QUALITY WITH AI – EXPERT SESSION

AI USE CASES: IMPROVING YIELD AND QUALITY

AI & ML driving sustainability

Explore the challenges of semiconductor production, including environmental concerns and strained supply chains. The article introduces an AI/ML-based solution developed by the Elisa IndustrIQ team to identify production failures early. This technology aims to make the semiconductor industry more resilient and sustainable.

Harnessing Machine Learning to Ease Semiconductor Shortage

This use case discusses Elisa IndustrIQ’s innovative use of machine learning to address the global semiconductor shortage. The solution creates a system to spot manufacturing deviations, improve yield, and prevent standstills. It also uses historical process data to identify corrective actions. The solution boosts throughput, reduces wastage, increases tool utilisation, and improves yield, thereby easing production bottlenecks and contributing significantly to the bottom line.

AI USE CASES: IMPROVING YIELD AND QUALITY

AI & ML driving sustainability

ai solutions - improving yield and quality

Explore the challenges of semiconductor production, including environmental concerns and strained supply chains. The article introduces an AI/ML-based solution developed by the Elisa IndustrIQ team to identify production failures early. This technology aims to make the semiconductor industry more resilient and sustainable.

Harnessing Machine Learning to Ease Semiconductor Shortage

This use case discusses Elisa IndustrIQ’s innovative use of machine learning to address the global semiconductor shortage. The solution creates a system to spot manufacturing deviations, improve yield, and prevent standstills. It also uses historical process data to identify corrective actions. The solution boosts throughput, reduces wastage, increases tool utilisation, and improves yield, thereby easing production bottlenecks and contributing significantly to the bottom line.