Resistive Ram for New Computing Systems: From Deep Learning to Biomimicry

Elisa Vianello


Resistive random-access memory (RRAM) is a memory technology that promises high-capacity, non-volatile data storage, low voltages, fast programming and reading time (few 10's of ns, even <1ns), single bit alterability, execution in place, good cycling performance (higher than Flash), density. Moreover RRAM can be easily integrated in the Back-End-Of-Line of advanced CMOS logic. This will revolutionize traditional memory hierarchy and facilitate the implementation of in-memory computing architectures and Deep Learning accelerators. To further improve the connectivity between memory arrays and computing, a combination of logic 3D Sequential Integration (3DSI) and memory arrays is a promising solution. Thanks to low processing thermal budget (<400°C), thermal stability (>500°C) and low cost (few additional masks), RRAM technologies are good candidates to be inserted in between sequentially stacked MOSFET tiers. RRAMs are also promising candidates for implementing energy-efficient bioinspired synapses, creating a path towards online real time unsupervised learning and life-long learning abilities. We will also explore the use of RRAM for future circuits and systems inspired by the emerging paradigm of biomimicry.