Where Power Is Spent In HBM


HBM is gaining ground because of a spike in the amount of data that needs to be processed quickly, but big reductions in power are possible if that processing can be moved closer to the HBM modules, and if more can be done in each compute cycle without sending data back and forth to memory as frequently. Steven Woo, fellow and distinguished engineer at Rambus, talks about what can be done to bo... » read more

HBM-based scalable multi-FPGA emulator for Quantum Fourier Transform (QFT)


New technical paper titled "A Scalable Emulator for Quantum Fourier Transform Using Multiple-FPGAs With High-Bandwidth-Memory" from researchers at Tohoku University in Japan. Abstract: "Quantum computing is regarded as the future of computing that hopefully provides exponentially large processing power compared to the conventional digital computing. However, current quantum computers do not... » read more

A Practical Approach To DFT For Large SoCs And AI Architectures, Part II


By Rahul Singhal and Giri Podichetty Part I of this article discusses the design-for-test (DFT) challenges of AI designs and strategies to address them at the die level. This part focuses on the test requirements of AI chips that integrate multiple dies and memories on the same package. Why 2.5D/3D chiplet-based designs for AI SoCs? Many semiconductor companies are adopting chiplet-based d... » read more

It’s Official: HBM3 Dons The Crown Of Bandwidth King


With the publishing of the HBM3 update to the High Bandwidth Memory (HBM) standard, a new king of bandwidth is crowned. The torrid performance demands of advanced workloads, with AI/ML training leading the pack, drive the need for ever faster delivery of bits. Memory bandwidth is a critical enabler of computing performance, thus the need for the accelerated evolution of the standard with HBM3 r... » read more

Advanced Packaging For Automotive Chips


Multiple types of chips may be better than one for dealing with large amounts and different types of data, but in automotive applications it's not entirely clear how or even whether they should be packaged together. The biggest problem with electronics in vehicles is the extreme range of temperatures, both within and outside of vehicles. Without adequate cooling, chips can age prematurely, s... » read more

Fan-Out And Packaging Challenges


Semiconductor Engineering sat down to discuss various IC packaging technologies, wafer-level and panel-level approaches, and the need for new materials with William Chen, a fellow at ASE; Michael Kelly, vice president of advanced packaging development and integration at Amkor; Richard Otte, president and CEO of Promex, the parent company of QP Technologies; Michael Liu, senior director of globa... » read more

Will Monolithic 3D DRAM Happen?


As DRAM scaling slows, the industry will need to look for other ways to keep pushing for more and cheaper bits of memory. The most common way of escaping the limits of planar scaling is to add the third dimension to the architecture. There are two ways to accomplish that. One is in a package, which is already happening. The second is to sale the die into the Z axis, which which has been a to... » read more

Fan-Out Packaging Options Grow


Chipmakers, OSATs and R&D organizations are developing the next wave of fan-out packages for a range of applications, but sorting out the new options and finding the right solution is proving to be a challenge. Fan-out is a way to assemble one or more dies in an advanced package, enabling chips with better performance and more I/Os for applications like computing, IoT, networking and sma... » read more

HBM Takes On A Much Bigger Role


High-bandwidth memory is getting faster and showing up in more designs, but this stacked DRAM technology may play a much bigger role as a gateway for both chiplet-based SoCs and true 3D designs. HBM increasingly is being viewed as a way of pushing heterogenous distributed processing to a completely different level. Once viewed as an expensive technology that only could be utilized in the hig... » read more

HBM2E Raises The Bar For AI/ML Training


The largest AI/ML neural network training models now exceed an enormous 100 billion parameters. With the rate of growth over the last decade on a 10X annual pace, we’re headed to trillion parameter models in the not-too-distant future. Given the tremendous value that can be derived from AI/ML (it is mission critical to five of six of the top market cap companies in the world), there has been ... » read more

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