(Invited) ALD Based Flexible Memristive Synapses for Neuromorphic Computing Application

Abstract

With the gradual development of artificial intelligence devices, the demand for data-intensive intelligent computation is increasing. The classical von Neumann computing scheme is suffering a bottleneck of complex information processing and storage[1]. In order to improve computing efficiency, In-memory Computing architectures were proposed to complete computing tasks based on storage units, including neuromorphic computing and logic-in-memory. On one hand, neuromorphic computing has become an attractive candidate due to the high efficiency by emulating the computing mode of human brain[2]. On the other hand, in-situ Boolean logic calculations can combine the arithmetic logic unit (ALU) and the storage unit to form memory-ALU (MALU), which could use the storage states of the memristor as the logic states to implement 16 basic Boolean logic gates[3]. Memristors with continuously adjustable conductance induced by voltage stimulation are suitable for simulating the weights update of bio-synapse[4]. Firstly, the ALD flexible artificial neural network (ANN) based on the integration of three-dimensional (3D) memristors network was proposed. Based on the structure of Pt/HfAlOx/TaN, the 3D ANN not only exhibits excellent memristive switching characteristics, but also achieves ultralow power synaptic characteristic (4.28 aJ/spike) under spike of 50 ns. The operation speed is 5 orders of magnitude higher than that of biological synapses, and the power consumption is 2-4 orders of magnitude lower than that of biological synapses. Besides, the 3D ANN has realized the function of information transmission including single channel and multi-channel, which could successfully transfer the letters “F”, “F” and “U” between the synapses of multilayer nerves. For the "0-9" digital images (28×28 pixels) in the MNIST database, the 3D ANN can complete the recognition processing, and the recognition rate can reach 88.8%. Finally, the in-memory computing device that integrates brain-like analog neuromorphic computing and digital logic computing was prepared, and its functions in low-power storage, brain-like neuromorphic computing, and in-situ Boolean logic computing were studied. In terms of logic calculation, the device can implement Boolean logic gates such as IMP, FALSE, and NAND by the combination and transitions of storage states, laying the foundation for its application in a multi-data type integrated in-memory computing system. This work provides effective guidelines for the development of ALD based devices for low power consumption in-memory computing system.