Search results for: 'Grid Interconnection study'

The Microelectronics Technology Opportunity Engine covers innovations pertaining to Microcontrollers, Low Power MCU’s, GaN and AI Interconnects.The Microelectronics Technology Opportunity Engine captures global electronics-related innovations and developments on a weekly basis. Developments are centered on electronics attributed by low power and co...

The study evaluates photonic and optical computing technologies as critical enablers of next-generation AI infrastructure, addressing growing bandwidth, latency, power consumption, and scalability constraints associated with copper-based interconnects. The analysis covers photonic integrated circuits (PICs), optical I/O chiplets, co-packaged optics...

This study focuses on heterogeneous computing architectures for automotive and robotics applications, examining how centralized and zonal compute architectures, heterogeneous SoCs integrating CPUs, GPUs, NPUs, DSPs, FPGAs, and dedicated safety islands, as well as robotics edge-AI controllers and mixed-criticality computing platforms, are enabling t...

This study focuses on how next-generation chiplet architectures, high-bandwidth memory (HBM), and advanced packaging technologies such as 2.5D/3D integration and fan-out wafer-level packaging are enabling the next wave of AI and high-performance computing (HPC) system architectures, rather than providing a broad packaging market overview. It analyz...

This study examines the technology landscape, commercialization progress, and future growth opportunities of wide-bandgap (WBG) semiconductor materials, with a primary focus on silicon carbide (SiC), gallium nitride (GaN), and gallium oxide (Ga₂O₃) for power and RF electronics. It evaluates how these materials enable higher efficiency, greater powe...

The Microelectronics Technology Opportunity Engine covers innovations pertaining to optoelectronics powering Hyperscale AI and Data Center Interconnects, Advanced Transceiver Chip For Increased Communication Efficiency, Low-Power System-on-Module (SoM) For Enhanced AI Applications, Next-generation Defect Review System with Faster Analysis of Nanosc...

Disruptive AI models are increasingly challenging traditional assumptions about “classic” cloud data centers. As training clusters scale up to thousands of accelerators and consume tens of megawatts of power, the conventional CPU-centric, air-cooled, monolithic server model is no longer sustainable. This research study explores the megatrend of AI ...

LiDAR technology has dramatically transformed in the last decade, creating significant demand for small, affordable, reliable, and sustainable LiDAR sensors. The need for efficiency, accuracy, depth mapping, low-light imaging, and videography in automotive, industrial, infrastructure, and consumer electronics products has driven LiDAR adoption acro...

This study examines the emerging landscape of neuromorphic computing and its role in enabling low-power, real-time, brain-inspired intelligence across edge and autonomous systems. It reviews advancements in spiking neural networks, memristor-based computing in memory, event-driven vision sensors, and hybrid neuromorphic and conventional architectur...