A Monolithic 3-D Integration of RRAM Array and Oxide Semiconductor FET for In-Memory Computing in 3-D Neural Network

We have monolithically integrated resistance random access memory (RRAM) array with oxide semiconductor channel access transistor in 3-D stack, achieved uniform memory characteristics of 1T1R cells at each layer, and demonstrated basic functionality of XNOR operation as in-memory computing for binary neural network (BNN) artificial intelligence (AI) applications. The integrated TiN/Ti/HfO
₂
/Ti stack RRAM shows high on/off ratio with stable retention and endurance properties. The indium gallium zinc oxide (IGZO) channel field-effect transistor (FET) works as an access transistor, which realized the low-temperature process, high mobility, and high drive current for RRAM. With the peripheral circuit, we demonstrate the XNOR operation using a pair of 1T1R cells. The impact of RRAM bit error rate on neural network is also investigated, which indicates the property of error-resilience in BNN. The 3-D neural network built by this architecture has high potential to enable area-efficient, low-power, and low-latency computing.