HOD HASHARON, Israel – Jan. 3, 2023 – Weebit Nano Limited (ASX:WBT), a leading developer of next-generation memory technologies for the global semiconductor industry, has taped-out (released to manufacturing) demonstration chips integrating its embedded Resistive Random-Access Memory (ReRAM or RRAM) module in an advanced 22nm FD-SOI (fully depleted silicon on insulator) process technology. This is the first tape-out of Weebit ReRAM in 22nm, one of the industry's most common process nodes, and a geometry where embedded flash is not viable. Weebit worked with its development partners CEA-Leti and CEA-List to successfully scale its ReRAM technology down to 22nm. The teams designed a full IP memory module that integrates a multi-megabit ReRAM block targeting the 22nm FD-SOI process which is designed to deliver outstanding performance for connected and ultra-low power applications such as IoT and edge AI. As embedded flash is unable to scale below 28nm, new non-volatile memory (NVM) technology is needed for smaller process geometries.

The 2020 Designcon conference included many talks and exhibits with a storage and memory focus. Both Rambus and Teledyne LeCroy had tutorials on design and connectivity for leading edge electronic components and systems as well as testing memory systems. This piece will look at some material from the tutorials and exhibits that can inform us about disaggregated processing developments, high speed chip to chip interfaces and memory for AI applications. Rambus said that there are multiple drivers to disaggregate semiconductor die, often called die disaggregation or chiplets. Creating specialized chips used with other chips in a package allows the introduction of specialized technologies (such as co-packaged optical systems) as well as better cooling solutions and greater in-package memory.

I used two of the hottest buzz words in one sentence: artificial intelligence and autonomous driving. It should be no surprise the two would be mentioned in the same sentence. Artificial intelligence and, specifically, the application of convolutional neural networks (CNNs) for image recognition has forever changed the automotive playing field by delivering higher levels of accuracy over more traditional computer vision algorithms. In fact, today's state-of-the-art image recognition algorithm--based on CNN--has been shown to deliver accuracy that is superior to humans. However, this accuracy comes at a price: highly accurate CNNs that are used to achieve level 5 autonomous driving can easily require 10s of 100s of teraflops of compute performance.

There's no doubt that artificial intelligence (AI) is becoming a key part of our lives as the technology finds its way into more and more real-world applications. In fact, the massive spurt in real-world AI use across several verticals -- including business services, healthcare, and automotive -- could boost AI revenue from just $3.2 billion in 2016 to almost $90 million by 2025, according to Tractica. There are several ways to buy into the AI market, with tech giants like NVIDIA and Alphabet among the top picks. AI deployment requires storage and processing of large amounts of data. For instance, self-driving cars that run with the help of AI gather data from several sensors and cameras, this data is then processed on the vehicle itself or is sent to the cloud.