Abstract:Fully degradable biomimetic synaptic devices based on a W/MgO/ZnO/Mo memristor on a silk protein substrate show remarkable information storage and synaptic characteristics.
“…For flexibility, neuromorphic electronics have been fabricated on paper substrates, [66,126] Si membrane, [127] and polymer substrates including poly(ethylene naphthalate) (PEN), [85,86,91,130,131] polyimide (PI), [64,81,132] poly(ethylene terephthalate) (PET), [4,[73][74][75]82,83,110,115,[133][134][135][136][137] chitosan membrane, [138] and silk. [139] Other components such as semiconductor layers and gate insulators have adopted the groups based on 2D materials, [91,131,135] organic materials, [4,73,74,82,83,91,110,133,136,140,141] metal oxide, [64,66,115,127,130,131,138,…”
Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next‐generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high‐density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.
“…For flexibility, neuromorphic electronics have been fabricated on paper substrates, [66,126] Si membrane, [127] and polymer substrates including poly(ethylene naphthalate) (PEN), [85,86,91,130,131] polyimide (PI), [64,81,132] poly(ethylene terephthalate) (PET), [4,[73][74][75]82,83,110,115,[133][134][135][136][137] chitosan membrane, [138] and silk. [139] Other components such as semiconductor layers and gate insulators have adopted the groups based on 2D materials, [91,131,135] organic materials, [4,73,74,82,83,91,110,133,136,140,141] metal oxide, [64,66,115,127,130,131,138,…”
Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next‐generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high‐density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.
“…Over the past two decades, there have always been new applications, which are not easy to realize with traditional electronics with inorganic semiconductors. It has resulted in pushing people to do more research on organic transistors [170,171], photovoltaic cells, energy harvester [61], light-emitting diodes [172], electronic devices [173], sensors [174], and memristors [20]. Compared with inorganic counterparts, organic technologies can address energy and cost inefficiency issues.…”
Featured Application: Thin-film materials with degradability and recyclability for the fabrication of degradable, dissolvable, resorbable, and/or compatible electronic devices, especially transient resistive switching devices for security information storage and neuromorphic computing as well as environmental-friendly applications.Abstract: The environmental pollution generated by electronic waste (e-waste), waste-gas, and wastewater restricts the sustainable development of society. Environmental-friendly electronics made of degradable, resorbable, and compatible thin-film materials were utilized and explored, which was beneficial for e-waste dissolution and sustainable development. In this paper, we present a literature review about the development of various degradable and disposable thin-films for electronic applications. The corresponding preparation methods were simply reviewed and one of the most exciting and promising methods was discussed: Printing electronics technology. After a short introduction, detailed applications in the environment sensors and eco-friendly devices based on these degradable and compatible thin-films were mainly reviewed, finalizing with the main conclusions and promising perspectives. Furthermore, the future on these upcoming environmental-friendly electronic devices are proposed and prospected, especially on resistive switching devices, showing great potential applications in artificial intelligence (AI) and the Internet of Thing (IoT). These resistive switching devices combine the functions of storage and computations, which can complement the off-shelf computing based on the von Neumann architecture and advance the development of the AI.
“…Copyright 2017, American Chemical Society. e–h) Reproduced with permission . Copyright 2018, Royal Society of Chemistry.…”
Section: Biocompatible Asdsmentioning
confidence: 99%
“…Dang et al used a different approach where they implemented a degradable biomimetic synaptic device based on a W/MgO/ZnO/Mo memristor with a silk protein substrate. Their devices were dissolved completely in phosphate‐buffered saline solution or deionized water within 7 min.…”
Section: Biocompatible Asdsmentioning
confidence: 99%
“…However, no single device may be labeled as perfect in this light. Inorganic‐based active layer materials placed on an organic substrate allow for an order of magnitude reduction in physical dimensions (although this might just be dictated by available resources and much smaller sizes may be achieved using advanced fabrication techniques) in comparison to the organic polymer‐based active layer devices. On the other hand, these devices had an operating current that was in the order of magnitude larger than the bio‐active layers, along with relatively higher programming voltage.…”
The state-of-the-art conception of a bionic/robotic eye is a somewhat bulky multipart system comprising a video camera connected to a processing unit that in turn communicates data through a wireless transmitter to either an in vivo retinal implant or a computer system. An artificial cogni-retina is a millimeterscale, intelligent apparatus designed as a replacement for these systems, while executing simple image processing tasks. As a bionic limb, it can connect directly to the optic nerve and perform rudimentary cognitive functions such as perceiving, learning, remembering, and classifying elementary visual data. This theoretical system presents a quantum leap in terms of size, power consumption, and speed to both prosthetic human eyes and robotic vision in artificial intelligence-based platforms such as autonomous vehicles. Recently, an increasing number of publications have used interesting materials in artificial synaptic devices that drive this idea closer toward becoming a real-world application. Such devices may form a basis for hardware-based deep learning artificial neural networks that can potentially execute image processing tasks within a single clock cycle compared to software algorithms running on conventional von Neumann machines that require millions of cycles to perform image sensor interfacing, memory fetch operations, and data path propagation.
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