A multiply-add engine with monolithically integrated 3D memristor crossbar/CMOS hybrid circuit

Abstract

Silicon (Si) based complementary metal-oxide semiconductor (CMOS) technology has been the driving force of the information-technology revolution. However, scaling of CMOS technology as per Moore’s law has reached a serious bottleneck. Among the emerging technologies memristive devices can be promising for both memory as well as computing applications. Hybrid CMOS/memristor circuits with CMOL (CMOS + “Molecular”) architecture have been proposed to combine the extremely high density of the memristive devices with the robustness of CMOS technology, leading to terabit-scale memory and extremely efficient computing paradigm. In this work, we demonstrate a hybrid 3D CMOL circuit with 2 layers of memristive crossbars monolithically integrated on a pre-fabricated CMOS substrate. The integrated crossbars can be fully operated through the underlying CMOS circuitry. The memristive devices in both layers exhibit analog switching behavior with controlled tunability and stable multi-level operation. We perform dot-product operations with the 2D and 3D memristive crossbars to demonstrate the applicability of such 3D CMOL hybrid circuits as a multiply-add engine. To the best of our knowledge this is the first demonstration of a functional 3D CMOL hybrid circuit.

Analogue signal and image processing with large memristor crossbars

“…Recently, pulse width, instead of amplitude, was used to represent the analogue input signals [27][28][29][30] , but this scheme requires more readout time and more complicated integrated circuits. Previous experimental demonstrations of an analogue-voltage-amplitude-vector by analogue-conductancematrix product, to the best of our knowledge, have been limited to a 1 × 3 system [24][25][26] , which is not strictly a VMM implementation. Here, we report completely analogue VMMs with adequate accuracy and high speed-energy efficiency that are based on up to 128 × 64 crossbars of hafnium oxide (HfO 2 ) memristors 36 , and experimentally demonstrate the important IoT and network edge applications of signal spectrum analysis, image compression and convolutional filtering.…”

Improving the Recognition Accuracy of Memristive Neural Networks via Homogenized Analog Type Conductance Quantization

“…Micromachines 2020, 11, 427 2 of 10 memristor-based in-memory computation was comparable to that of the complementary metaloxide-semiconductor (CMOS) platforms [22,23]. Shrinking the dimension of the conductive filaments (CFs) into the atomic scale of quantum point contact allows memristor ballistic electron transport without scattering and quantized conductance (QC) characteristics in analog domains [24][25][26]. It not only significantly increases the data storage capacity of the devices, but also allows stronger information processing ability in neuromorphic systems.…”

Resistive Memory‐Based In‐Memory Computing: From Device and Large‐Scale Integration System Perspectives

“…[38,89,91] The new concept of the 3D synapse also has emerged, using the multiple layer of 3D RRAM arrays to store the synaptic weights and conduct matrix multiplication. [38,89,91] The new concept of the 3D synapse also has emerged, using the multiple layer of 3D RRAM arrays to store the synaptic weights and conduct matrix multiplication.…”