Foundries See Growth, New Issues In 2019


The silicon foundry business is poised for growth in 2019, although the industry faces several challenges across a number of market segments next year. Generally, foundry vendors saw steady growth in 2018, but many are ending the year on a sour note. Weak demand for Apple’s new iPhone XR and a downturn in the cryptocurrency market have impacted several IC suppliers and foundries, causing t... » read more

Foundries Prepare For Battle At 22nm


After introducing new 22nm processes over the last year or two, foundries are gearing up the technology for production—and preparing for a showdown. GlobalFoundries, Intel, TSMC and UMC are developing and/or expanding their efforts at 22nm amid signs this node could generate substantial business for applications like automotive, IoT and wireless. But foundry customers face some tough choic... » read more

Memory Startups To Watch


The next-generation memories are finally ramping up after years’ of delays and promises. Intel, for one, is shipping 3D XPoint, a next-generation technology based on phase-change memory. In addition, the big foundries are readying embedded MRAM. And, of course, there are a number of other players in the next-generation memory arena. There are also a number of startups that are flying un... » read more

Next-Gen Memory Ramping Up


The next-generation memory market is heating up as vendors ramp a number of new technologies, but there are some challenges in bringing these products into the mainstream. For years, the industry has been working on a variety of memory technologies, including carbon nanotube RAM, FRAM, MRAM, phase-change memory and ReRAM. Some are shipping, while others are in R&D. Each memory type is di... » read more

Embedded Flash Scaling Limits


Embedded nonvolatile flash memory has played a key role in chips for years, but the technology is beginning to face some scaling and cost roadblocks and it’s not clear what comes next. Embedded flash is used in several markets, such as automotive, consumer and industrial. But the automotive sector appears to be the most concerned about the future of the technology. Typically, a car incorpo... » read more

What’s Next In Neuromorphic Computing


To integrate devices into functioning systems, it's necessary to consider what those systems are actually supposed to do. Regardless of the application, [getkc id="305" kc_name="machine learning"] tasks involve a training phase and an inference phase. In the training phase, the system is presented with a large dataset and learns how to "correctly" analyze it. In supervised learning, the data... » read more

A New Memory Contender?


Momentum is building for a new class of ferroelectric memories that could alter the next-generation memory landscape. Generally, ferroelectrics are associated with a memory type called ferroelectric RAMs (FRAMs). Rolled out by several vendors in the late 1990s, FRAMs are low-power, nonvolatile devices, but they are also limited to niche applications and unable to scale beyond 130nm. While... » read more

The Next 5 Years Of Chip Technology


Semiconductor Engineering sat down to discuss the future of scaling, the impact of variation, and the introduction of new materials and technologies, with Rick Gottscho, CTO of [getentity id="22820" comment="Lam Research"]; Mark Dougherty, vice president of advanced module engineering at [getentity id="22819" comment="GlobalFoundries"]; David Shortt, technical fellow at [getentity id="22876" co... » read more

Pushing DRAM’s Limits


If humans ever do create a genuinely self-aware artificial intelligence, it may well exhibit the frustration of waiting for data arrive. The access bandwidth of DRAM-based computer memory has improved by a factor of 20x over the past two decades. Capacity increased 128x during the same period. But latency improved only 1.3x, according to Kevin Chang, a researcher at Carnegie Mellon Universit... » read more

3D Neuromorphic Architectures


Matrix multiplication is a critical operation in conventional neural networks. Each node of the network receives an input signal, multiplies it by some predetermined weight, and passes the result to the next layer of nodes. While the nature of the signal, the method used to determine the weights, and the desired result will all depend on the specific application, the computational task is simpl... » read more

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