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Going Vertical With GaN Devices


Gallium nitride has long been on the horizon for a variety of uses in semiconductors, but implementing this on a commercial scale has been relatively slow due to a variety of technical hurdles. That may be about to change. The wide bandgap of GaN makes it particularly attractive material for power conversion applications. Still, actually realizing its benefits in commercial devices has been ... » read more

IC Materials For Extreme Conditions


The number of materials being researched for chips used in extreme environments, such as landing on the planet Venus, is growing. While GaN has captured much of the attention for power conversion circuits, it's just one of several applications for semiconductors in extreme environments. The high voltage, high temperature, and caustic atmospheres found in many industrial and aerospace environ... » read more

Power Converter Chip Research Booms


Power electronics are booming, fueled by demand ranging from induction chargers for wearable and portable electronics, to charging stagings for electric vehicles. An estimated 80% of all U.S. electricity will pass through some form of power converter by 2030, said Yogesh Ramadass, director of power management at Texas Instruments' Kilby Labs. Transportation applications, in particular, deman... » read more

Stronger, Better Bonding In Advanced Packaging


System-in-package integrators are moving toward copper-to-copper direct bonding between die as the bond pitch goes down, making the solder used to connect devices in a heterogenous package less practical. In thermocompression bonding, protruding copper bumps bond to pads on the underlying substrate. In hybrid bonding, copper pads are inlaid in a dielectric, reducing the risk of oxidation. In... » read more

The Darker Side Of Hybrid Bonding


With semiconductors, it's often things everyone takes for granted that cause the biggest headaches, and that problem is compounded when something fundamental changes — such as bonding two chips together using a process aimed at maximizing performance. Case in point: CMP for backend of the line metallization in hybrid bonding. While this is a mature process, it doesn't easily translate for ... » read more

Bonding Issues For Multi-Chip Packages


The rising cost and complexity of developing chips at the most advanced nodes is forcing many chipmakers to begin breaking up that chip into multiple parts, not all of which require leading edge nodes. The challenge is how to put those disaggregated pieces back together. When a complex system is integrated monolithically — on a single piece of silicon — the final product is a compromise ... » read more

Neural Networks Without Matrix Math


The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren't the only path forward. Almost all commercial machine learning applications depend on artificial neural networks, which are trained using large datasets with a back-propagation algorithm. The network first analyzes a training example, typically assign... » read more

Zeroing In On Biological Computing


Artificial spiking neural networks need to replicate both excitatory and inhibitory biological neurons in order to emulate the neural activation patterns seen in biological brains. Doing this with CMOS-based designs is challenging because of the large circuit footprint required. However, researchers at HP Labs observed that one biologically plausible model, the Hodgkins-Huxley model, is math... » read more

Spiking Neural Networks Place Data In Time


Artificial neural networks have found a variety of commercial applications, from facial recognition to recommendation engines. Compute-in-memory accelerators seek to improve the computational efficiency of these networks by helping to overcome the von Neumann bottleneck. But the success of artificial neural networks also highlights their inadequacies. They replicate only a small subset of th... » read more

Challenges For Compute-In-Memory Accelerators


A compute-in-memory (CIM) accelerator does not simply replace conventional logic. It's a lot more complicated than that. Regardless of the memory technology, the accelerator redefines the latency and energy consumption characteristics of the system as a whole. When the accelerator is built from noisy, low-precision computational elements, the situation becomes even more complex. Tzu-Hsian... » read more

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