Pt 1: Maintaining the Physics Model within AI & ML

Pt 1: Maintaining the Physics Model within AI & ML

Arnaud Hubaux, ASML


In this episode of the True North Podcast, IpX Director of MBE, Max Gravel and Senior Technical Program Manager, ASML, Arnaud Hubaux discuss the challenges of maintaining the physics model in AI/ML with rapidly changing datasets. 

Arnaud gives a detailed review of how ASML uses AI to deliver greater value through new products for their customers and to accelerate manufacturing processes by qualifying high performing parts. Max and Arnaud discuss ASML’s unique approach to machine learning solutions with customers and how AI optimizes production and performance. Hear Arnaud break down:

  • The challenges and necessity of explainability in AI/ML — where a causal clarification is needed between the observed effects and what is really happening physically in order to improve future design
  • Ensuring continual guaranteed performance upon installation and after deployment of their machines to manage drift and ensure dataset accuracy 
  • The challenge and criticality of safety for people and materials first
  • Defining and maintaining baselines consisting of thousands of parameters between both design and production
  • Understanding the change impact to physics models: how to perform validation and verifications activities on the physics models before changes are applied to the customer product in order to mitigate risks with machine behavior.

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