Weebit Nano

RRAM developer Weebit Nano announced that it has raised $50 million AUD (about $32.4 million USD) through a strongly supported underwritten placement to institutional investors.

The company says that funds will be used to expand the company’s commercialization activities, support new customer and partner agreements, and accelerate the development of its cutting-edge RRAM technology.

In October 2023 RRAM developer Weebit Nano announced that it has signed a licensing agreement with DB HiTek, a Korean-based foundry (said to be one of the world's top 10 foundries). The commercial agreement includes technology transfer, qualification and licensing.

Yesterday Weebit announced together with DB HiTek that it has taped-out (released to manufacturing) a demonstration chip integrating Weebit’s embedded RRAM module in DB HiTek’s 130nm Bipolar-CMOS-DMOS (BCD) process. The highly integrated demo chips will be used for testing and qualification ahead of customer production, while demonstrating the performance and robustness of Weebit’s technology.

Israel-based RRAM developer Weebit Nano announced a new partnership with Efabless, a creator platform for semiconductor chips, to enable faster and more cost-effective prototype creation of new System-on-Chip (SoC) designs using Weebit’s RRAM technology.

The agreement provides Efabless’ thousands of users and customers with access to Weebit’s IP, which can be incorporated into their designs manufactured using SkyWater Technology Foundry’s 130nm CMOS process. Weebit’s RRAM technology is already proven and qualified up to 125C in SkyWater Technology’s US production fab.

Israel-based RRAM developer Weebit Nano announced that it has received the first wafers integrating its
embedded RRAM memory manufactured in GlobalFoundries’ 22FDX platform. Initial tests of the wafers in this advanced 22nm FD-SOI (fully depleted silicon on insulator) are showing positive results on the array.

Weebit says that these are the first wafers with Weebit ReRAM manufactured in 22nm, one of the industry’s most
steadfast process nodes, and a geometry where embedded flash is not viable. 

Israel-based RRAM developer Weebit Nano announced that it has received its first IP licensing revenue for its embedded ReRAM technology.

This initial licensing fee is $100,000. Weebit’s plan is to generate revenues from foundries and product companies in three main ways: licensing fees for design and manufacture; engineering fees for designs and processes; and royalties for use by customers, which will be received once customers begin shipping end products.

Israel-based RRAM developer Weebit Nano announced that it has taped-out (released to manufacturing) demonstration chips integrating its embedded RRAM module in an advanced 22nm FD-SOI (fully depleted silicon on insulator) process technology.

The company says that this is its first tape-out in 22 nm, 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.

Israel-based RRAM developer Weebit Nano announced that it has received the silicon wafers from SkyWater Technology, with embedded Weebit RRAM. The company says this is an important step as its technology is now proven with standard tools and a mature process flow, which makes it easy for customers to dopt its technology.

The silicon SkyWater RRAM-enabled wafers will be sliced into chips, packaged, and then tested and qualified. The chips, which were manufactured in SkyWater’s 130nm CMOS process, will be used for customer demonstrations, testing and prototyping ahead of commercial orders and volume production, allowing customers to confidently start designing SoCs using these ReRAM modules.

Israel-based RRAM developer Weebit Nano demonstrated (together with its partner, CEA-Leti) how its RRAM can provide an exciting new solution for artificial intelligence applications using spiking neural networks, at the Flash Memory Summit:

Neuromorphic computing makes it possible to emulate the brain’s natural operation. Using RRAM, this emulation is much more efficient than today’s simulations, consuming orders of magnitude less power, to allow sophisticated AI at a fraction of the power consumed by today’s systems. This is because the ReRAM (RRAM) cell has physical and functional similarities to a biological brain synapse.