Memristor‐Based Intelligent Human‐Like Neural Computing

Abstract

In the field of a relatively dangerous working environment, NASA developed Valkyrie, a 44-degree-of-freedom, series elastic actuator-based robot. In addition, Valkyrie is designed to respond to disasters like nuclear disasters and advance human spaceflight in extraterrestrial planetary settings. [6] By implementing safety features and allowing remote intervention, Atkeson et al. enabled an Atlas humanoid robot to meet the standard in performing disaster response-related tasks involving physical contact with the environment. [7] Currently, to allow humanoid robots to feel and process the environmental information as human beings for perfectly implementing tasks, perceptual comprehension and computation efficiency are two key indexes. Nevertheless, it is challenging to achieve them. The former requires the installation of massive, diverse sensors in humanoids, which will inevitably slow down the processing speed under the current sensing-storage-process separated framework, i.e., von Neumann architecture. In other words, the current computer architecture builds up a barrier between sensing performance and computation efficiency. In this context, neuromorphic devices which can emulate the perceptual and computation functions of the biological nervous system have illustrated their potential to break the von Neumann barrier, attracting researchers' interests (Figure 1). Humanoid robots, intelligent machines resembling the human body in shape and functions, cannot only replace humans to complete services and dangerous tasks but also deepen the own understanding of the human body in the mimicking process. Nowadays, attaching a large number of sensors to obtain more sensory information and efficient computation is the development trend for humanoid robots. Nevertheless, due to the constraints of von Neumann-based structures, humanoid robots are facing multiple challenges, including tremendous energy consumption, latency bottlenecks, and the lack of bionic properties. Memristors, featured with high similarity to the biological elements, play an important role in mimicking the biological nervous system. The memristor-based nervous system allows humanoid robots to obtain high energy efficiency and bionic sensing properties, which are similar properties to the biological nervous system. Herein, this article first reviews the biological nervous system and memristor-based nervous system thoroughly, including the structures and also the functions. The applications of memristor-based nervous systems are introduced, the difficulties that need to be overcome are put forward, and future development prospects are also discussed. This review can hopefully provide an evolutionary perspective on humanoid robots and memristor-based nervous systems.

Porous crystalline materials for memories and neuromorphic computing systems

“…, artificial synapses and artificial neurons) is crucial for constructing neuromorphic computing systems capable of overcoming the von Neumann bottleneck in this post-Moore's law era. 71,72,74,75,89,393,394,479,480 Up to now, various devices, including memristor, 70,72,73,481–488 flash memory, 285,489–492 EG-FET, 293,295,296,489,490,493–496 and memtransistor, 497–499 based on different functional materials, 484,500,501 such as 2D materials, 85–88,387,502–508 perovskite, 76–80,389,509,510 biomaterials, 81,82 TMO, 385,511–513 and organic materials, 71,90,514,515 have been utilized for neuromorphic devices.…”

Designing polar textures with ultrafast neuromorphic features from atomistic simulations

“…Finally, the third type of the polar materials is a classical ferroelectric system and, more specifically, ultrathin films made of lead-zirconium-titanate, PbZr 1−x Ti x O 3 (PZT). 'Normal' ferroelectrics have already been exploited for neuromorphic functionalities by considering the tunability of the ratio between up and down ferroelectric domains under electric field pulses [19]. Here, we rather exploit their ability to exhibit a variety of exotic polar textures, such as stripe-like dipole pattern, vortices, bubble, skyrmions, etc, depending on electrical and mechanical boundary conditions [20].…”

Coexistence mechanisms of negative differential resistance and resistive switching effects in a WOx-based memristor

“…8,9 Additionally, synaptic plasticity and biological cognitive behavior based on memristors can be analyzed to build brain-like neural computing systems. 10–12…”

“…8,9 Additionally, synaptic plasticity and biological cognitive behavior based on memristors can be analyzed to build brain-like neural computing systems. [10][11][12] Recently, an important physical phenomenon of the coexistence of the negative differential resistance (NDR) effect and resistance switching (RS) effect was found in analysis of some metal oxide memristors. [13][14][15]22 NDR is an abnormal phenomenon in which the current decreases with increase in voltage.…”