AI Semiconductor Chips Need AI SuperpowersAI Semiconductor Chips Need AI Superpowers

In the wake of generative AI, unprecedented demand for data and computing power is outstripping capabilities. This is driving the semiconductor industry’s return to growth but without better chips, the next phase of progress won’t unfold. To achieve this, we need new materials capable of enabling higher-performance devices.

 Inflection Points in Materials Research Methodologies

Traditionally the discovery of new materials for semiconductor chips relies on arduous, iterative, time-consuming and costly lab synthesis and testing processes back and forth between semiconductor makers, tool manufacturers and material suppliers. With AI pushing the demand for semiconductors to unprecedented levels, this model is challenged to meet the demand for the rapid, co-optimized and efficient introduction and scaling of device and materials technology at higher levels of abstraction. 

Current technological challenges require materials suppliers to introduce innovative new solutions to meet the unprecedented complexity of chip architectures, which is driving them to dramatically accelerate development and learning. Material suppliers are uniquely positioned to spearhead these efforts to accelerate innovation, while also helping to mitigate the risks associated with the introduction of new materials.

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Enter AI for Materials Discovery and Intelligence

There’s exciting progress underway to revolutionize materials discovery by identifying new materials and optimizing materials intelligence for greater efficiencies. Scientists and engineers are using the most advanced digital tools—AI, machine learning and more—coupled with sophisticated capabilities that allow companies in the semiconductor supply chain to test, validate, innovate and accelerate the enablement of AI and machine learning capabilities.

This focus on the co-optimization of devices and materials using advanced device testing capabilities harnesses the potential of AI in scientific discovery. This, in turn, drives further AI advancements and accelerates innovation across various fields. 

Generative AI is being used to model new molecules and formulations in a way never seen before to leverage chemistry and molecular science discovery. Additionally, it can predict material behaviors under various conditions, identify optimal material compositions for specific applications and enhance manufacturing efficiencies. New innovations are also leading to the development of more efficient devices to enable AI, for example, through DRAM capacitor stack engineering and atomic layer etching (ALE).

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In the new era of device technology, innovation is no longer solely determined by device size but by the ability to create more intelligence within the material and through architectural improvements such as neuro-inspired computing. Leveraging data and AI enables confident decision-making in designing new materials atom by atom throughout the entire R&D workflow—from ideation to experimentation. The result: Better, faster, smarter chips to power the AI revolution.