Freestanding Artificial Synapses Based on Laterally Proton‐Coupled Transistors on Chitosan Membranes

Liu, Yanghui; Zhu, Li; Feng, Ping; Shi, Yi

doi:10.1002/adma.201502719

Cited by 384 publications

(326 citation statements)

References 34 publications

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“…[123,[140][141][142] Also, FETs based on silicon, organic materials, perovskite or carbon nanotube (CNT) have been reported as synaptic elements based on either charge trapping/detrapping mechanism or floating-gate (FG) memory structure, some of which have a structure of multi-gate FET. [120][121][122][123][124][125][126][127][128] Three-terminal FeFET and two-terminal ferroelectric tunnel junction (FTJ) relying on ferroelectric polarization switching have been explored as artificial synapses, where the pulsing scheme needs to be carefully designed to improve the switching symmetry and linearity. [94,103] Similarly, two-terminal spin-transfer torque MRAM (STT-MRAM) based on magnetization switching and multi-terminal magnetic devices based on domain wall motion have also been studied to implement artificial synapses.…”

Section: Artificial Synapsesmentioning

confidence: 99%

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

et al. 2019

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As the research on artificial intelligence booms, there is broad interest in brain‐inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re‐visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.

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Section: Artificial Synapsesmentioning

confidence: 99%

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

et al. 2019

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“…Two-terminal memristors or three-terminal transistors can emulate the functions of biological synapses due to their similar transmission characteristics and have been proposed to realize neuromorphic computers. [2][3][4][5][6][7][8] The reproducible gradual tuning of resistance/conductivity of the memristor represents device. Such a simple sandwich structure is good for building large-scale synaptic arrays.…”

Section: Introductionmentioning

confidence: 99%

Energy‐Efficient Hybrid Perovskite Memristors and Synaptic Devices

Xiao

Huang

2016

Adv Elect Materials

350

371

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the synaptic plasticity, which is the basis of remembering and learning in the brain. [1] However, not all the functions discovered in biological synapses have been realized on single artificial synaptic devices with energy consumption of femto-Joule per event, which has limited the building of neuromorphic computing systems. [2,9] Organometal trihalide perovskites (OTPs) including methylammonium lead iodide (MAPbI 3 ) have recently emerged as a new generation of earth abundant semiconductor materials for solar cell applications, [10][11][12][13][14][15] which have demonstrated a high certified efficiency over 20% [10] due to their superior optoelectronic properties of strong absorption, [16] unusual defect physics, [17] extremely low trap density, and long carrier diffusion length. [18] Besides, they have also been demonstrated for applications in other electronic devices, light emitting diodes, [19] laser, [20] photo detectors. [21] In this work, we demonstrated low-temperature, solutionprocessed two-terminal MAPbI 3 devices as ideal candidates of memristors and synaptic devices to mimic the biological synapses. The twoterminal OTP synaptic devices can mimic the neuromorphic learning and remembering process. Many functions of biological synapses have been visualized in the perovskite synaptic devices including four forms of spike-timing-dependent plasticity (STDP), spike-rate-dependent plasticity (SRDP) short-term plasticity (STP) and long-term potentiation (LTP)), and learningexperience behavior etc. The perovskite synapse has potential of low energy consumption of femto-Joule/(100 nm) 2 per event due to the switchable p-i-n structure and low activation energy for the ion migration in OTP material. [22] A novel phenomenon of photo-read SRDP was observed, which offer a novel readout method using light in addition to the electric pulse. Figure 1a shows the structure of the device with perovskite layer sandwiched between two electrodes poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) covered indium tin oxide (ITO) and gold (Au), which have comparable work functions. PEDOT:PSS is considered as an anode and Au as a cathode for the simplicity of following discussion. It is a typical metal-semiconductor-metal (MSM) structure New parallel computing architectures based on neuromorphic computing are needed due to their advantages over conventional computation with regards to real-time processing of unstructured sensory data such as image, video, or voice. However, developing artificial neuromorphic system remains a challenge due to the lack of electronic synaptic devices, which can mimic all the functions of biological synapses with low energy consumption. Here it is reported that two-terminal organometal trihalide perovskite (OTP) synaptic devices can mimic the neuromorphic learning and remembering process. Various functions known in biological synapses are demonstrated in OTP synaptic devices including four forms of spike-timing-dependent plasticity (STDP), spike-rate-dependent plasticity (SRDP), sh...

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“…Chitosan can be transformed into chitin and has the same excellent characteristics, such as biocompatibility, biodegradability, non-toxicity, antimicrobial activity, and an outstanding film-forming ability [16,17,18]. Therefore, in previous studies, chitosan has also been used as dielectric layer in organic transistors [19,20]. Yttrium (III) oxide (Y 2 O 3 ) is a type of rare earth oxide that is non-toxic, thermodynamically stabile, stabile at high temperature (T m = 2430 °C), and has a high dielectric constant (ε = 15~18), light transparency, and a linear transmittance in the infrared spectra.…”

Section: Introductionmentioning

confidence: 99%

Eco-Friendly and Biodegradable Biopolymer Chitosan/Y2O3 Composite Materials in Flexible Organic Thin-Film Transistors

Singh

et al. 2017

Materials

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Abstract:The waste from semiconductor manufacturing processes causes serious pollution to the environment. In this work, a non-toxic material was developed under room temperature conditions for the fabrication of green electronics. Flexible organic thin-film transistors (OTFTs) on plastic substrates are increasingly in demand due to their high visible transmission and small size for use as displays and wearable devices. This work investigates and analyzes the structured formation of aqueous solutions of the non-toxic and biodegradable biopolymer, chitosan, blended with high-k-value, non-toxic, and biocompatible Y 2 O 3 nanoparticles. Chitosan thin films blended with Y 2 O 3 nanoparticles were adopted as the gate dielectric thin film in OTFTs, and an improvement in the dielectric properties and pinholes was observed. Meanwhile, the on/off current ratio was increased by 100 times, and a low leakage current was observed. In general, the blended chitosan/Y 2 O 3 thin films used as the gate dielectric of OTFTs are non-toxic, environmentally friendly, and operate at low voltages. These OTFTs can be used on surfaces with different curvature radii because of their flexibility.

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Freestanding Artificial Synapses Based on Laterally Proton‐Coupled Transistors on Chitosan Membranes

Cited by 384 publications

References 34 publications

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Energy‐Efficient Hybrid Perovskite Memristors and Synaptic Devices

Eco-Friendly and Biodegradable Biopolymer Chitosan/Y2O3 Composite Materials in Flexible Organic Thin-Film Transistors

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