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.

Rain Neuromorphics has taped out a demonstration chip for its brain-inspired analog architecture that employs a 3D array of randomly-connected memristors to compute neural network training and inference at extremely low power. Switching to entirely analog hardware for AI computation could allow a massive reduction in the power consumed by AI workloads. While some commercial chips currently use analog processor-in-memory techniques, they require digital conversion between network layers, consuming significant power. The limitations of current analog devices also means they can't be used for training AI models since they are incompatible with back-propagation, the algorithm widely used for AI training. Rain's aim is to build a complete analog chip, solving these issues with a combination of new hardware and a new training algorithm.