Chips

Power converters based on gallium nitride (GaN) are progressing swiftly owing to their exceptional efficiency and tiny dimensions, boosted by high power density and fast switching capabilities. Nevertheless, these benefits are accompanied by considerable thermal management issues that impact reliability, performance, and operational lifespan. This review examines advanced thermal management approaches for high-power-density GaN power converters, including active and passive cooling technologies, sophisticated packaging designs, and the use of novel materials like graphene and diamond to improve heat dissipation. The impacts of thermal boundary resistance, self-heating phenomena, and substrate selection on thermal performance are thoroughly analyzed. Strategies for enhancing printed circuit board (PCB) layouts, thermal vias, and the use of thermal interface materials (TIMs) are also emphasized. The study highlights co-design approaches that optimize thermal resistance and layout efficiency, supporting GaN operation under high-frequency conditions. This thorough investigation offers insights into addressing the thermal challenges linked to GaN technology, promoting its adoption in forthcoming power devices.

Neuromorphic computing, an interdisciplinary field combining neuroscience and computer science, aims to create efficient, bio-inspired systems. Different from von Neumann architectures, neuromorphic systems integrate memory and processing units to enable parallel, event-driven computation. By simulating the behavior of biological neurons and networks, these systems excel in tasks like pattern recognition, perception, and decision-making. Neuromorphic computing chips, which operate similarly to the human brain, offer significant potential for enhancing the performance and energy efficiency of bio-inspired algorithms. This review introduces a novel five-dimensional comparative framework—process technology, scale, power consumption, neuronal models, and architectural features—that systematically categorizes and contrasts neuromorphic implementations beyond existing surveys. We analyze notable neuromorphic chips, such as BrainScaleS, SpiNNaker, TrueNorth, and Loihi, comparing their scale, power consumption, and computational models. The paper also explores the applications of neuromorphic computing chips in artificial intelligence (AI), robotics, neuroscience, and adaptive control systems, while facing challenges related to hardware limitations, algorithms, and system scalability and integration.

Semiconductor technologies are susceptible to radiation effects. The particle incidence in susceptible areas of an integrated circuit (IC) can generate physical interactions capable of producing errors. This paper predicts the IC cross sections for Single Event Effects. The cross section is a metric that provides an IC’s susceptibility to radiation. It deals with particle source interaction and physical design volumes. This work evaluates the IC cross section, exploring the physical design characteristics of susceptible regions in logic gates. It explores particles with low LET, identifying the charge collection areas. Also, the heavy ions are used to evaluate the critical cross section range. Distinct benchmark circuits were simulated to characterize sensitivity trends. The influence of circuit input conditions along with cells’ susceptibility reveals significant findings. The results indicate a difference up to ten times between low- and high-energy particles. Consequently, predicting the IC cross section at an early stage of the design flow is essential, especially for electronics devices used in radiation environments.