Long-Haul Trucking With Fewer Drivers


The trucking industry is betting heavily on increasing levels of autonomy and electrification to reduce the cost of moving goods and to overcome persistent problems. The economics of autonomous driving are compelling, not least of which is an almost perpetual shortage of qualified drivers. But there also are a number of technical hurdles to making this work. On top of the challenges facing t... » read more

Wrestling With Analog At 3nm


Analog engineers are facing big challenges at 3nm, forcing them to come up with creative solutions to a widening set of issues at each new process node. Still, these problems must be addressed, because no digital chip will work without at least some analog circuitry. As fabrication technologies shrink, digital logic improves in some combination of power, performance, and area. The process te... » read more

Tradeoffs Between Edge Vs. Cloud


Increasing amounts of processing are being done on the edge, but how the balance will change between what's computed in the cloud versus the edge remains unclear. The answer may depend as much on the value of data and other commercial reasons as on technical limitations. The pendulum has been swinging between doing all processing in the cloud to doing increasing amounts of processing at the ... » 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

New Memories Add New Faults


New non-volatile memories (NVM) bring new opportunities for changing how we use memory in systems-on-chip (SoCs), but they also add new challenges for making sure they will work as expected. These new memory types – primarily MRAM and ReRAM – rely on unique physical phenomena for storing data. That means that new test sequences and fault models may be needed before they can be released t... » read more

Fabs Drive Deeper Into Machine Learning


Advanced machine learning is beginning to make inroads into yield enhancement methodology as fabs and equipment makers seek to identify defectivity patterns in wafer images with greater accuracy and speed. Each month a wafer fabrication factory produces tens of millions of wafer-level images from inspection, metrology, and test. Engineers must analyze that data to improve yield and to reject... » read more

Making Test Transparent With Better Data


Data is critical for a variety of processes inside the fab. The challenge is getting enough consistent data from different equipment and then plugging it back into the design, manufacturing, and test flows to quickly improve the process and uncover hard-to-find defective die. Progress is being made. The inspection and test industry is on the cusp of having more dynamic ways to access the dat... » read more

Will Automotive Ethernet Win?


As internal combustion engines are replaced by electric motors, and mechanical linkages increasingly replaced by electronic messaging, an in-vehicle network is needed to facilitate communication. Ethernet, amended for automotive and other time-sensitive applications, appears to be the network of choice. But is that choice a done deal? And will Ethernet replace all other in-car networks? The ... » read more

Grappling With Smart City Security Issues


Security concerns are rising as cities seek to modernize services by connecting them to the internet and to each other, creating a widening attack surface that is a potential target for everything from disruption of services to ransomware demands. The goal of smart cities is to apply technology and intelligence to a variety of services to enable independent operation, real-time response, as ... » read more

Why TinyML Is Such A Big Deal


While machine-learning (ML) development activity most visibly focuses on high-power solutions in the cloud or medium-powered solutions at the edge, there is another collection of activity aimed at implementing machine learning on severely resource-constrained systems. Known as TinyML, it’s both a concept and an organization — and it has acquired significant momentum over the last year or... » read more

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