Every day more devices become connected to each other, to networks, and to the cloud – delivering valuable insights into everything from our personal health to our farms to city infrastructure and beyond. The IoT depends on massive numbers of small sensors reliably delivering streams of data from often remote or inaccessible locations. To enable these billions of battery-operated and energy harvesting IoT edge devices, embedded NVM must have:
Weebit’s ReRAM enables these requirements for embedded NVM in IoT, with significantly higher performance and lower power than embedded Flash, and much lower cost compared to alternative emerging NVM technologies. Weebit’s ReRAM also maintains high endurance and data retention in harsh field conditions.
Analog and Power ICs
Analog ICs represent a diverse set of technologies including power management ICs (PMICs), mixed-signal, RF, MEMS, and others. These devices increasingly require embedding low-density, high-endurance NVM. They must support many components, most of which are built in Front-end-of-line (FEOL) processes, and they must support many process nodes and derivatives. Since embedded Flash is also integrated in FEOL, today companies must make compromises with power and analog components. This leads to degraded performance, larger size, and higher cost.
ReRAM is integrated in Back-end-of-line (BEOL) processes, allowing full optimization of such components, so there is no impact on design rules. ReRAM also enables optimized performance and size.
In addition, some analog and power management designs today require the integration of a microcontroller (MCU) for added intelligence, such as smart sensing and measurement on end nodes in areas such as medical and fitness devices, industrial automation and other ultra-low power IoT applications. These highly integrated devices not only need to store data and boot code, but must also run OTA firmware updates, requiring high density, high-performance, low-power NVM.
As a low-cost, high-performance, highly scalable BEOL technology, Weebit’s ReRAM enables key requirements for embedded NVM in analog ICs.
Storage Class Memory (SCM)
As data usage and storage needs increase, the industry is looking for new solutions to address storage class memory (SCM). There is a big gap in today’s systems between the main memory (DRAM), which is very fast but volatile, and storage (Flash/SSD), which is very slow and non-volatile. To address this gap in SCM, a new type of memory, Persistent Memory (PM), is now emerging.
PM is faster and lower power than Flash and less expensive than DRAM. It is also non-volatile, and is targeted to accelerate storage in data centers, cloud computing and mass storage devices, as well as AI. Weebit’s ReRAM is a perfect fit for PM since it not only reduces cost compared to DRAM, but can deliver higher performance, better endurance and lower latency than NAND Flash.
As we move from 4G to 5G wireless communications, the demands on network infrastructure equipment are rising sharply to accommodate not just broadband communications, but also a wide range of emerging applications in areas such as autonomous vehicles, smart cities and industrial IoT. This means there is a growing need throughout the network for all types of memory, including NVM.
The characteristics of ReRAM, with lower read latency, faster write performance and lower power consumption compared to Flash, make it an ideal fit for the networks of tomorrow. Weebit’s ReRAM delivers the manufacturability, scalability, performance, endurance and data retention needed, while making devices more energy efficient.
Weebit’s ReRAM cell functions similarly to a synapse in the brain, making it a promising solution for neuromorphic computing, which drives AI. Many institutes are now studying this domain, which has the potential to become a huge market in the future.
Weebit is collaborating with research partners – both academia and industry – to explore the possibilities of using ReRAM for neuromorphic computing. Together with our partner CEA-Leti, we were the first in the industry to demonstrate ReRAM-based spiking neural networks (SNN).