The Best DRAMs For Artificial Intelligence


Artificial intelligence (AI) involves intense computing and tons of data. The computing may be performed by CPUs, GPUs, or dedicated accelerators, and while the data travels through DRAM on its way to the processor, the best DRAM type for this purpose depends on the type of system that is performing the training or inference. The memory challenge facing engineering teams today is how to keep... » read more

Challenges In Using Sub-7nm ICs In Automotive


The automotive industry is producing vehicles with increasing levels of real-time decision-making, enabled by thousands of ICs, sensors, and multi-chip packages, but making sure these systems work flawlessly throughout their expected lifetimes is a growing challenge. Automotive chips traditionally were developed at mature process nodes in five- to seven-year cycles, but much has changed over... » read more

Rethinking Chip Reliability For Harsh Conditions


As semiconductors push into environments once considered untenable, reliability expectations are being redefined. From the vacuum of space and the inside of jet engines to deep industrial automation and electrified drivetrains, chips now must endure extreme temperature swings, corrosive atmospheres, mechanical vibration, radiation, and unpredictable power cycles, all while delivering increasing... » read more

High-Quality Data Needed To Better Utilize Fab Data Streams


Fab operations have wrestled with big data management issues for decades. Standards help, but only if sufficient attention to detail is taken during collection. Semiconductor wafer manufacturing represents one of the most complex manufacturing processes in the world. With each generation of process improvement comes more sophisticated fab equipment, new process recipes, and exponential incre... » read more

How Secure Are Analog Circuits?


The move toward multi-die assemblies and the increasing value of sensor data at the edge are beginning to focus attention and raise questions about security in analog circuits. In most SoC designs today, security is almost entirely a digital concern. Security requirements in digital circuits are well understood, particularly in large data centers and at the upper end of edge computing, which... » read more

Mobile Chip Challenges In The AI Era


Leading smart phone vendors are struggling to keep pace with the rising compute and power demands of localized generative AI, standard phone functions, and the need to move more data back and forth between handsets and the cloud. In addition to edge functions, such as facial recognition and other on-device apps, phones must accommodate a continuous stream of new communications protocols, and... » read more

AR/VR Glasses Taking Shape With New Chips


More augmented reality (AR), virtual reality (VR), and mixed reality (MR) wearables are coming, but how they are connected, and where image and other data is processed, are still in flux. Ray-Ban Meta AI glasses, for example, look like classic eyeglasses, but they rely on a tethered smart phone for such functions as taking pictures, AI voice assistance, and object identification. In contrast... » read more

Connecting AI Accelerators


Experts At The Table: Semiconductor Engineering sat down to discuss the various ways that AI accelerators are being applied today with Marc Meunier, director of ecosystem development at Arm; Jason Lawley, director of product marketing for AI IP at Cadence; Paul Karazuba, vice president of marketing at Expedera; Alexander Petr, senior director at Keysight; Steve Roddy, chief marketing office... » read more

Co-Packaged Optics Reaches Power Efficiency Tipping Point


Commercialization has started for network switches based on co-packaged optics (CPO), which are capable of routing signals at terabits per second speeds, but manufacturing challenges remain regarding fiber-to-photonic IC alignment, thermal mitigation, and optical testing strategies. By moving the optical-to-electronic data conversion as close as possible to the GPU/ASIC switch in data center... » read more

Optimizing Data Movement


Demand for new and better AI models is creating an insatiable demand for more processing power and much better data throughput, but it's also creating a slew of new challenges for which there are not always good solutions. The key here is figuring out where bottlenecks might crop up in complex chips and advanced packages. This involves a clear understanding of how much bandwidth is required ... » read more

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