University College London spins out ReRAM startup

March 14, 2018 // By Peter Clarke
University College London spins out ReRAM startup
Intrinsic Semiconductor Technologies Ltd. has been established to commercialize silicon-dioxide resistive RAM devices developed by Professor Tony Kenyon and Adnan Mehonic, at University College London.

UCL's research has followed a similar path to that of Professor James Tour at Rice University, which is also based on the ability to switch the resistance of thin layers of silicon oxide (see UK researchers follow silicon-oxide ReRAM route and Rice University: Making memory out of silicon oxide). Professor Tour's work has been instrumental in the formation of Weebit Nano Ltd. (see Weebit moves SiOx ReRAM on to 40nm).

Intrinsic is led by Vassilios Albanis, business manager at the UCL Business incubation service, while Professor Kenyon serves as chief scientific officer and Mehonic is chief technology officer.

Silicon dioxide is used extensively as an insulator in IC fabrication but the ability to switch resistance of very thin layers of the material with low energy and make it a conductor with a higher cycling endurance than flash memory makes it a highly promising candidate for ReRAM.

Intrinsic's approach is based on the formation of nanometre-scale conductive filaments within the SiOx. These filaments can then be made and broken through the application of voltages of less than 1.5V. The technology can also be tuned for specific applications such as: embedded memory where it offers low power, cycling endurance and scalablity; automotive applications where its high temperature operation is valued; aerospace where it offers radiation hardness; and machine learning and neuromorphic computing.

For machine learning where matrix multiplication is used in the implementation of neural networks there is the prospect that simple cross-point geometry can provide tailored precision and accuracy at low power with a memory-centric architecture. Intrinsic's devices also display characteristics associated with neurons that can be used in neuromorphic computing. These include synaptic plasticity, spiking behaviour, the ability to process spike-encoded data; thresholding and integration functions.

Next: From embedded memory to neuromorphic systems

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