This review article summarizes the key areas of self-cleaning coatings, primarily focusing on various materials that are widely used in recent research and also in commercial applications. The scope of this article orbits around hydrophobic and hydrophilic coatings, their working mechanism, fabrication techniques that enable the development of such coatings, various functions like Anti-icing, Electro-wetting, Surface switchability and the areas where selfcleaning technology can be implemented. Moreover, different characterization techniques and material testing feasibilities are also analyzed and discussed. Though several companies have commercialized a few products based on self-cleaning coating technology, much potential still remains in this field.

A review on self-cleaning coatings

SiO2 is one of the most abundant materials on Earth. It is cost-effective and also environmentally benign when used as an energy material. Although SiO2 was inactive to Li, it was engineered to react directly by a simple process. It exhibited a strong potential as a promising anode for Li-ion batteries.

Quartz (SiO2): a new energy storage anode material for Li-ion batteries

We study the paired-pulse-induced response of a NiOx-based memristor. The behavior of the memristor is surprisingly similar to the paired-pulse facilitation of a biological synapse. When the memristor is stimulated with a pair of electrical pulses, the current of the memristor induced by the second pulse is larger than that by the first pulse. In addition, the magnitude of the facilitation decreases with the pulse interval, while it increases with the pulse magnitude or pulse width.

/pdf/emulating-the-paired-pulse-facilitation-of-a-biological-1zmf4vfz65.pdf

Emulating the paired-pulse facilitation of a biological synapse with a NiOx-based memristor

The well-known Ebbinghaus forgetting curve, which describes how information is forgotten over time, can be emulated using a NiO-based memristor with conductance that decreases with time after the application of electrical pulses. Here, the conductance is analogous to the memory state, while each electrical pulse represents a memory stimulation or learning event. The decrease in the conductance with time depends on the stimulation parameters, including pulse height and width and the number of pulses, which emulates memory loss behavior well in that the time taken for the memory to be lost depends on how the information is learned.

/pdf/emulating-the-ebbinghaus-forgetting-curve-of-the-human-brain-1li2c8hqyy.pdf

Emulating the Ebbinghaus forgetting curve of the human brain with a NiO-based memristor

A striking correlation between infrared photoinduced absorption spectra and the photoluminescence from silicon nanocrystals indicates that quantized electronic sublevels of the nanocrystals are resonantly coupled to surface vibrational modes via a polarization field produced by coherent longitudinal polar vibrations. Our experimental results and model support the assumption that the mechanism responsible for the efficient photoluminescence from silicon nanocrystals should be assigned to inhibition of nonradiative channels rather than enhancement of radiative channels.

Resonant coupling between surface vibrations and electronic states in silicon nanocrystals at the strong confinement regime.

Batch normalization-free weight-binarized SNN based on hardware-saving IF neuron

A quantitative approach to determination of depth profiles of optical properties of Si-implanted SiO2 films based on spectroscopic ellipsometry (SE) is presented. From the SE measurements, the depth profiles of the complex refractive index of SiO2 films containing Si nanocrystals (Si-nc) are obtained with an effective medium approximation (EMA) in the wavelength range of 400-1200nm. The optical profiles obtained imply the existence of a wave-guide in the Si-doped SiO2 films.

Optical-Property Profiling of SiO2 Films Containing Si Nanocrystals Formed by Si+ Implantation

Recently, Ge nanocluster or nanocrystal for the next-generation non-volatile memory (NVM) devices has received much attention. Although there is enormous research on the Ge-nanocrystal based NVM, there are few studies on the charging effect on the current conduction in the gate stack. As revealed by this work, the charging has a significant impact on current conduction in the gate stack and thus it may affect the NVM performance. This paper discusses in detail the effect of charging in nc-Ge.

Charging effect in germanium nanocrystals embedded in a SiO2 matrix

As a crucial component of neuromorphic chips, on-chip memory usually occupies most of the on-chip resources and limits the improvement of neuron density. The alternative of using off-chip memory may result in additional power consumption or even a bottleneck for off-chip data access. This article proposes an on- and off-chip co-design approach and a figure of merit (FOM) to achieve a trade-off between chip area, power consumption, and data access bandwidth. By evaluating the FOM of each design scheme, the scheme with the highest FOM (1.085× better than the baseline) is adopted to design a neuromorphic chip. Deep multiplexing and weight-sharing technologies are used to reduce on-chip resource overhead and data access pressure. A hybrid memory design method is proposed to optimize on- and off-chip memory distribution, which reduces on-chip storage pressure and total power consumption by 92.88% and 27.86%, respectively, while avoiding the explosion of off-chip access bandwidth. The co-designed neuromorphic chip with ten cores fabricated under standard 55 nm CMOS technology has an area of 4.4 mm 2 and a core neuron density of 4.92 K/mm 2, an improvement of 3.39  ∼ 30.56× compared with previous works. After deploying a full-connected and a convolution-based spiking neural network (SNN) for ECG signal recognition, the neuromorphic chip achieves 92% and 95% accuracy, respectively. This work provides a new path for developing high-density and large-scale neuromorphic chips.

Achieving High Core Neuron Density in a Neuromorphic Chip Through Trade-Off Among Area, Power Consumption, and Data Access Bandwidth.

Both isolated Si nanocrystals (nc-Si) dispersedly distributed in a SiO 2 matrix and densely stacked nc-Si layers embedded
in SiO 2 have been synthesized with the ion implantation technique followed by high temperature annealing. The
dielectric functions of the isolated nc-Si and densely-stacked nc-Si layer embedded in SiO 2 have been determined with
spectroscopic ellipsometry (SE) in the photon energy range of 1.1-5 eV. The dielectric functions of these two different Si
nanostructures were successfully extracted from the SE fitting based on a multi-layer fitting model that takes into
account the distribution of nc-Si in SiO2 and a five phase model (i.e., air/SiO 2 layer/densely-stacked nc-Si layer/SiO 2 
layer/Si), respectively. The dielectric spectra of isolated nc-Si distributed in SiO 2 present a two-peak structure, while the
dielectric spectra of densely-stacked nc-Si layer show a single broad peak, being similar to that of amorphous Si. The
dielectric functions of these two Si nanostructures both show significant suppressions as compared with bulk crystalline
Si. However, it has been observed that the densely stacked nc-Si layer exhibits a more significant suppression in the
dielectric spectra than the isolated nc-Si dispersedly embedded in SiO 2 . This is probably related to the two factors: (i) the
nc-Si size (~3 nm) of the densely stacked nc-Si layer is smaller than that (~4.5 nm) of the isolated nc-Si embedded in
SiO 2 matrix, and (ii) the densely stacked nc-Si layer has an amorphous phase.

A comparative study on the dielectric functions of isolated Si nanocrystals and densely stacked Si nanocrystal layer embedded in SiO2 synthesized with Si ion implantation

Y. Liu

Papers

Batch normalization-free weight-binarized SNN based on hardware-saving IF neuron

Optical-Property Profiling of SiO2 Films Containing Si Nanocrystals Formed by Si+ Implantation

Charging effect in germanium nanocrystals embedded in a SiO2 matrix

Achieving High Core Neuron Density in a Neuromorphic Chip Through Trade-Off Among Area, Power Consumption, and Data Access Bandwidth.

A comparative study on the dielectric functions of isolated Si nanocrystals and densely stacked Si nanocrystal layer embedded in SiO2 synthesized with Si ion implantation