Multiple Approaches To Memory Challenges


As we enter the era of Big Data and Artificial Intelligence (AI), it is amazing to think about the possibilities for a truly seismic shift in the changing requirements for memory solutions. The massive amount of data humans generate every year is astounding and yet is expected to increase five-fold in the next few years from machine-generated data. Further compounding this growth is the emergin... » read more

The Promise Of GDDR6 And 7nm


Research Nester, a market research and consulting firm, estimates that the “global market of computer graphics may witness a remarkable growth and reach at the valuation of $215.5 billion by the end of year 2024.” Plus, it says this market is expected to grow at a significant compound annual growth rate or CAGR of 6.1% over the forecast period 2017 to 2024. Computer graphics is just the ... » read more

Unsticking Moore’s Law


Sanjay Natarajan, corporate vice president at Applied Materials with responsibility for transistor, interconnect and memory solutions, sat down with Semiconductor Engineering to talk about variation, Moore's Law, the impact of new materials such as cobalt, and different memory architectures and approaches. What follows are excerpts of that conversation. SE: Reliability is becoming more of an... » read more

Using Memory Differently


Chip architects are beginning to rewrite the rules on how to choose, configure and use different types of memory, particularly for chips with AI and some advanced SoCs. Chipmakers now have a number of options and tradeoffs to consider when choosing memories, based on factors such as the application and the characteristics of the memory workload, because different memory types work better tha... » read more

Realizing the Benefits of 14/16nm Technologies


The scaling benefits of Moore’s Law are challenging below 28nm. It is no longer a given that the cost per gate will go down at process nodes below 28nm, e.g., 20nm though 14nm and 7nm. Rising design and manufacturing costs are contributing factors to this trend. Meanwhile, the competing trend of fewer but more complex system-on-chip (SoC) designs is reducing the knowledge base of many chip... » read more

Week In Review: Manufacturing, Test


Chipmakers Shares of Intel fell amid lackluster results for the company, according to a report from CNBC. But Intel is also boosting its capital spending to $15.5 billion, according to the report. Here’s more on Intel from PC World. Meanwhile, Intel is expanding its research fab in Oregon, dubbed D1X, according to a report from The Oregonian. The company is in the process of building an ... » read more

Fearless chip and fab tool forecasts


2019 is expected to be a challenging, if not confusing, year for the semiconductor and fab equipment industries. For example, Apple recently issued a warning about lackluster smartphone demand, which impacted several IC vendors and foundries. Then, the memory market is plummeting. In addition, the 10nm/7nm transition has proven to be difficult for many. And let’s not forget the geopolitica... » 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

Much Ado About Memory


New semiconductor applications are ever changing and improving our lives, from new smartphones and wearables to healthcare, factory automation, and artificial intelligence. The humble memory chip working in the background plays a critical role in enabling these technologies. For example, that awesome picture you just took would be lost forever without memory. Your computer can’t perform the i... » read more

Power/Performance Bits: Jan. 22


Efficient neural net training Researchers from the University of California San Diego and Adesto Technologies teamed up to improve neural network training efficiency with new hardware and algorithms that allow computation to be performed in memory. The team used an energy-efficient spiking neural network for implementing unsupervised learning in hardware. Spiking neural networks more closel... » read more

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