Memory Issues For AI Edge Chips


Several companies are developing or ramping up AI chips for systems on the network edge, but vendors face a variety of challenges around process nodes and memory choices that can vary greatly from one application to the next. The network edge involves a class of products ranging from cars and drones to security cameras, smart speakers and even enterprise servers. All of these applications in... » read more

What’s WAT? Testing At The End Of Manufacturing


The high costs of building, resourcing and operating a foundry fabricating integrated circuits are well known. Fabless companies avoid this capital cost and focus on design and innovation in their area of expertise. On the other hand, the fabless company relies on the expertise and skills of the foundry to produce quality wafers. Many times a process used by a fabless company to manufacture... » read more

Logic Chip, Heal Thyself


If a single fault can kill a logic chip, that doesn’t bode well for longevity of complex multi-chip systems. Obsolescence in chips is not just an industry ploy to sell more chips. It is a fact of physics that chips don’t last more than a few years, especially if overheated, and hit with higher voltage than it can stand. The testing industry does a great job finding defects during manufac... » read more

Manufacturing Bits: Dec. 16


Imec-Leti alliance At the recent IEEE International Electron Devices Meeting (IEDM), Imec and Leti announced plans to collaborate in select areas. The two R&D organizations plan to collaborate in two areas—artificial intelligence (AI) and quantum computing. Imec and Leti have been separately working on AI technologies based on various next-generation memory architectures. Both entitie... » read more

Magnetic Memories Reach For Center Stage


Wearable heart rate sensors. Networked smoke detectors. Smart lighting. Smart doorbells. While desktop computers and even smartphones are powerful standalone tools, Internet of Things devices share a need to collect data from the environment, store it, and transmit it to some other device for action or further analysis. In many systems, data storage and working memory account for the majorit... » read more

Accurate Error Bit Mode Analysis Of STT-MRAM Chip With A Novel Current Measurement Module


Authors: (Advantest) Ryo Tamura, Ibuki Mori Naoyoshi Watanabe; (Tohoku University) Hiroki Koike, Tetsuo Endoh. A novel memory test system is needed for future STT-MRAM mass production that supports error bit analysis and its mode categorization on STT-MRAM chip measurement, as STTMRAM cell’s switching is a probabilistic phenomenon based on quantum mechanics. In order to meet this requireme... » read more

Challenges In Making And Testing STT-MRAM


Several chipmakers are ramping up a next-generation memory type called STT-MRAM, but there are still an assortment of manufacturing and test challenges for current and future devices. STT-MRAM, or spin-transfer torque MRAM, is attractive and gaining steam because it combines the attributes of several conventional memory types in a single device. In the works for years, STT-MRAM features the ... » read more

More Memory And Processor Tradeoffs


Creating a new chip architecture is becoming an increasingly complex series of tradeoffs about memories and processing elements, but the benefits are not always obvious when those tradeoffs are being made. This used to be a fairly straightforward exercise when there was one processor, on-chip SRAM and off-chip DRAM. Fast forward to 7/5nm, where chips are being developed for AI, mobile ph... » read more

Embedded Phase-Change Memory Emerges


The next-generation memory market for embedded applications is becoming more crowded as another technology emerges in the arena—embedded phase-change memory. Phase-change memory is not new and has been in the works for decades. But the technology has taken longer to commercialize amid a number of technical and cost challenges. Phase-change memory, a nonvolatile memory type that stores data... » read more

AI Architectures Must Change


Using existing architectures for solving machine learning and artificial intelligence problems is becoming impractical. The total energy consumed by AI is rising significantly, and CPUs and GPUs increasingly are looking like the wrong tools for the job. Several roundtables have concluded the best opportunity for significant change happens when there is no legacy IP. Most designs have evolved... » read more

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