Leveraging AI/ML to Increase Capacity in Mature Semiconductor Manufacturing Environments

The next phase for Industry 4.0 initiatives will be to aggregate ML models to provide fab-wide predictions such as Wafer Acceptance Testing.

Powering the Quantum Leap

The role of cryogenic wafer probing in applications such as cryogenic quantum computing and supra-conductive CMOS semiconductors, where temperatures near absolute zero are essential, is discussed.

The Human Hand: Curating Good Data and Creating an Effective Deep-Learning R2R Strategy for High-Volume Manufacturing

Areas ideally suited for AI applications may be repetitive and mental labor-intensive tasks with good available data, or when analytical methods are not applicable or too difficult to find, or data interpolation.

AI, Cameras and Your Smartphone: Computing at the Edge and On the Go

An AI-powered camera using a dedicated co-processor chip with innovative deep learning algorithms can deliver a vision-based solution with unmatched performance, power efficiency, cost-effectiveness, and scalability.

Survey: Understanding the Data-Centric Era

The Data-Centric Era is here. It demands highly flexible, high bandwidth and secure infrastructure to meet the demands of highly variable, large and diverse data. Composable infrastructure provides the flexibility, bandwidth and efficiency to meet that demand.

New Insight into Two Widely Accepted Forms of Deep Learning

Experts at BrainChip offered new insights and considerations for the use of transfer learning and incremental learning in edge AI/IoT environments.