Novel Device and Materials in Emerging Memory for Neuromorphic Computing
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore novel device technologies and materials for emerging memory in neuromorphic computing in this tinyML talk. Delve into the development of new hardware solutions to overcome Moore's law scaling limits and enable efficient data-driven computing with high reliability. Learn about self-rectified memory, two-dimensional memory, self-aligned helical-structured materials, and thermal stability investigations for next-generation memory. Discover how these advancements contribute to neuromorphic computational applications and address challenges like sneak path current in array applications. Examine the fabrication process of bilayer selectorless RRAM, array size considerations, and read margin improvements. Investigate the detection of resistive switching gaps, mechanisms behind bilayer selectorless RRAM, and its benchmarking. Gain insights into synaptic behavior for neuromorphic computing, memory window effects with radiation, and temperature impacts during training and inference processes. Read more