Semiconductor Engineering

A Practical Approach To DFT For Large SoCs And AI Architectures, Part II

Part I of this article discusses the design-for-test (DFT) challenges of AI designs and strategies to address them at the die level. This part focuses on the test requirements of AI chips that ...
EurekAlert!

Machine-learning guides discovery of multi-principal element alloys as electrocatalyst for hydrogen evolution reaction

This study develops an ML-assisted approach for accelerating the discovery of HER electrocatalysts in multi-principal element alloys. With ultralow material costs and record-breaking electrocatalytic ...
C&EN

Machine learning helps density functional theory model molecules accurately

A rotating cylinder with its side cut away to expose the core, showing patches of purple, blue, green, yellow, and orange that are dense in the middle and more diffuse toward the edges. This rotating ...
EurekAlert!

Figure 4: Density Functional Theory (DFT) calculations of CO₂ hydrogenation on the surfaces of pure iron sulfide and manganese-doped iron sulfide. (IMAGE)

Figure 3: Simulated reaction of metal-doped iron sulfides catalyzing the H₂-driven reduction of CO₂ under various terrestrial hot spring conditions. Figure 4: Density Functional Theory (DFT) ...
Semiconductor Engineering

A Practical Approach To DFT For Large SoCs And AI Architectures, Part I

The traditional processors designed for general-purpose applications struggle to meet the computing demands and power budgets of artificial intelligence (AI) or machine leaning (ML) applications.