Multi-chip module (MCM) graphics cards just might be the future, bringing upcoming generations of GPUs to a whole new level. Combining multiple dies instead of relying on a single chip could provide ...

Osram Opto Semiconductors has introduced the first compact laser multi-chip package. The PLPM4 450 module can pack up to 20 blue laser chips into a single "butterfly" package for projection ...

Built 70% smaller than its predecessor, the ROK104001 Bluetooth multi-chip module has been developed to speed time-to-market by enabling designers to quickly implement qualified and tested Bluetooth ...

Qorvo, Inc. has launched three new highly integrated RF multi-chip modules (MCMs) designed for advanced radar applications. Leveraging Qorvo’s advanced packaging and process technology, the QPF5001, ...

Tesla has been granted a patent for a multi-chip module design featuring a Redistribution Layer substrate with IC dies mounted on both surfaces, sockets, and a cold plate. The innovative mounting ...

[Willem Melching] owns a 2010 Volkswagen Golf – a very common vehicle in Europe – and noticed that whilst the electronic steering rack supports the usual Lane Keep Assist (LKAS) system, and would be ...

Historically, GPUs have been designed as monolithic dies with all of their functionality under one 'roof.' This hasn't always been the case -- the earliest GPUs sometimes used separate chips for ...

For high performance applications, demand for highly integrated packages has increased. This is due to the highly integrated package’s electrical performance advantages of reduction of interchip ...

TL;DR: Apple's iPhone 18 will feature the next-gen A20 chip using TSMC's advanced WMCM packaging with MUF technology, enhancing efficiency and yield. Eternal secured a major contract as a packaging ...

“More than Moore technologies can be supported by system-level diversification enabled by chiplet-based integrated systems within multichip modules (MCMs) and silicon interposer-based 2.5-D systems.

Why it matters: Currently available deep learning resources are falling behind the curve due to increasing complexity, diverging resource requirements, and limitations imposed by existing hardware ...