Monolithic 3D Integration of Analog RRAM-Based Computing-in-Memory and Sensor for Energy-Efficient Near-Sensor Computing.

Abstract

In the era of the Internet of Things, vast amounts of data generated at sensory nodes impose critical challenges on the data-transfer bandwidth and energy efficiency of computing hardware. A near-sensor computing (NSC) architecture places the processing units closer to the sensors such that the generated data can be processed almost in situ with high efficiency. This study demonstrates the monolithic three-dimensional (M3D) integration of a photosensor array, analog computing-in-memory (CIM), and Si complementary metal-oxide-semiconductor (CMOS) logic circuits, named M3D-SAIL. This approach exploits the high-bandwidth on-chip data transfer and massively parallel CIM cores to realize an energy-efficient NSC architecture. The 1st layer of the Si CMOS circuits serves as the control logic and peripheral circuits. The 2nd layer comprises a 1k-bit one-transistor-one-resistor (1T1R) array with InGaZnOx field-effect transistor (IGZO-FET) and resistive random-access memory (RRAM) for analog CIM. The 3rd layer comprises multiple IGZO-FET-based photosensor arrays for wavelength-dependent optical sensing. The structural integrity and function of each layer are comprehensively verified. Furthermore, NSC is implemented using the M3D-SAIL architecture for a typical video keyframe-extraction task, achieving a high classification accuracy of 96.7% as well as a 31.5× lower energy consumption and 1.91× faster computing speed compared to its2D counterpart.