An artificial olfactory system with sensing, memory and self-protection capabilities

Zhao

et al. 2022

Adv Funct Materials

Self Cite

The simulation of human feelings, perceptions, and actions has become an important application field for medical treatment, human-computer interfaces, and intelligent robots. However, the need for various functional units hinders system integration. A threshold-switching memristor is used as the sympathetic nerve center to simulate an unconditioned reflex. High current nonlinearity (10 6 ) is achieved by adopting a nanocontact structure, and high flexibility (bending radius of ≈5 mm) is obtained by combining the structure with a flexible polymer film. Furthermore, integrating a flexible sensor and a flexible artificial muscle made of ionic polymer-metal composite allows to form an all-flexible complete reflex arc of an unconditioned reflex. The threshold-switching memristor can provide strain-dependent control through simple control logic, indicating the great potential of adopting thresholdswitching memristors for primary neuromorphological control and flexible intelligent medical treatment.

Section: Resultsmentioning

confidence: 99%

All‐Flexible Artificial Reflex Arc Based on Threshold‐Switching Memristor

Zhao

et al. 2022

Adv Funct Materials

Self Cite

“…Unlike two-terminal memristors, the basic transistor structure usually adopts multiple-terminal configuration, and superior weight controllability as well as advanced synaptic functions of nonvolatile in-memory computing devices can be realized by additional input terminals and versatile device configurations. [221][222][223][224][225][226][227][228][229][230] It also bears mentioning that we should distinguish these transistor-based nonvolatile in-memory computing system from the volatile in-memory computing primitives based on, for example, dynamic random access memory (DRAM) and static random access memory (SRAM). Although the CMOS-based DRAM and SRAM are mature commercialized techniques, their slow scaling trend and limited capacitance have motivated the research focus toward nonvolatile in-memory computing technology.…”

Section: D In-memory Computing Architecture Based On Transistorsmentioning

confidence: 99%

Bio‐Inspired 3D Artificial Neuromorphic Circuits

Liu

et al. 2022

Adv Funct Materials

Neuromorphic circuits emulating the bio‐brain functionality via artificial devices have achieved a substantial scientific leap in the past decade. However, even with the advent of highly advanced bio‐inspired algorithms, the artificial intelligence based on current neuromorphic circuits is lagging behind significantly when compared with naturally evolved biological neural circuits. This massive and intriguing discrepancy is partly due to the incomprehensive understanding of bio‐brain operating mechanism, which relies heavily on the extremely complexed entangled 3D hierarchical neural networks. Configuring 3D neuromorphic hardware with combined computing and memory functionalities, coupled with compatible progress of software algorithms, can be an inevitable route to surmount the limitation encountered by current 2D artificial circuits. Herein, referring to the neuron configuration in 3D perspective together with detailed signal generation and propagation mechanism, the von Neumann configuration is compared with state‐of‐the‐art in‐memory computing architecture, and the development and perspectives of 3D in‐memory computing neuromorphic circuits are highlighted.

“…The system classifies seven common gases, such as lemon juice and steam from white vinegar, as shown in Figure 11C, it identifies gases by comparing raw sensor data with previously learned expected responses. Gao et al proposed a synthetic odor using Sr-ZnO based gas sensors (Gao et al, 2021), HfO x based memristors, and electrochemical actuators. In addition to gas identification, the Sr-ZnO gas sensor can take some measures to protect the system from toxic gases.…”

Section: Olfactory Perception Systemmentioning

confidence: 99%

Emerging Memristive Devices for Brain-Inspired Computing and Artificial Perception

Zhu

et al. 2022

Front. Nanotechnol.

Brain-inspired computing is an emerging field that aims at building a compact and massively parallel architecture, to reduce power consumption in conventional Von Neumann Architecture. Recently, memristive devices have gained great attention due to their immense potential in implementing brain-inspired computing and perception. The conductance of a memristor can be modulated by a voltage pulse, enabling emulations of both essential synaptic and neuronal functions, which are considered as the important building blocks for artificial neural networks. As a result, it is critical to review recent developments of memristive devices in terms of neuromorphic computing and perception applications, waiting for new thoughts and breakthroughs. The device structures, operation mechanisms, and materials are introduced sequentially in this review; additionally, late advances in emergent neuromorphic computing and perception based on memristive devices are summed up. Finally, the challenges that memristive devices toward high-performance brain-inspired computing and perception are also briefly discussed. We believe that the advances and challenges will lead to significant advancements in artificial neural networks and intelligent humanoid robots.