Search results for: 'computing plan back'

The October 2025 edition of the CST Technology Opportunity Engine highlights transformative innovations shaping the future of edge computing. The study explores the evolution of decentralized data processing models that enable ultra-low latency, localized intelligence, and real-time decision-making across industries.Driven by the convergence of IoT...

The Microelectronics Technology Opportunity Engine provides profiles of innovations related to High-qubit Quantum Computing Platform, Energy-efficient and Scalable AI Accelerators, High-performance and Scalable Quantum Computers, Sustainable Tensor Processing Technology, Modular Quantum Computing Architecture, Turnkey High-performance Quantum Compu...

12 Mar 2025  |  Global  |  Technology Research

Emerging Semiconductor Technologies in the Manufacturing Industry

Innovative Materials and Chips Transforming Smart Manufacturing

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The semiconductor industry is pivotal to enabling next-generation technologies, such as quantum computing, AI, and machine learning (ML) accelerators, transforming manufacturing processes. Quantum chips are being explored for their potential to solve complex optimization problems, while AI and ML accelerators are enabling faster data processing and...

The additive manufacturing sector has seen significant growth in recent years owing to growing demand for customized products and complex geometries in various sectors, including automotive, defense, aerospace, consumer electronics, manufacturing, and energy. AI has a wide range of applications in additive manufacturing at various levels and is a k...

The Microelectronics Technology Opportunity Engine covers innovations pertaining to memory systems such as DRAM, MRAM, and other memory technologies for improved performance, advancements in materials enabling next generation chipsets, 3.5D System-in-Package Platform For Enhanced AI Training, GaN Device for Improved Motor Energy Efficiency and Low-...

The edge AI accelerator market is scaling rapidly as industries demand low-latency, privacy-aware intelligence for autonomous vehicles, industrial automation, smart cameras, robotics, and other applications. Cloud-based inference faces more limits from bandwidth, latency, and data sovereignty concerns, creating a strong pull for on-device AI proces...