Multiaxial and Transparent Strain Sensors Based on Synergetically Reinforced and Orthogonally Cracked Hetero‐Nanocrystal Solids

Wearable strain sensors are widely researched as core components in electronic skin. However, their limited capability of detecting only a single axial strain, and their low sensitivity, stability, opacity, and high production costs hinder their use in advanced applications. Herein, multiaxially highly sensitive, optically transparent, chemically stable, and solution‐processed strain sensors are demonstrated. Transparent indium tin oxide and zinc oxide nanocrystals serve as metallic and insulating components in a metal–insulator matrix and as active materials for strain gauges. Synergetic sensitivity‐ and stability‐reinforcing agents are developed using a transparent SU‐8 polymer to enhance the sensitivity and encapsulate the devices, elevating the gauge factor up to over 3000 by blocking the reconnection of cracks caused by the Poisson effect. Cross‐shaped patterns with an orthogonal crack strategy are developed to detect a complex multiaxial strain, efficiently distinguishing strains applied in various directions with high sensitivity and selectivity. Finally, all‐transparent wearable strain sensors with Ag nanowire electrodes are fabricated using an all‐solution process, which effectively measure not only the human motion or emotion, but also the multiaxial strains occurring during human motion in real time. The strategies can provide a pathway to realize cost‐effective and high‐performance wearable sensors for advanced applications such as bio‐integrated devices.     

Well‐Defined Functional Linear Aliphatic Diblock Copolyethers: A Versatile Linear Aliphatic Polyether Platform for Selective Functionalizations and Various Nanostructures

Low‐temperature anionic ring‐opening homopolymerizations and copolymerizations of two glycidol derivatives (allyl glycidyl ether (AGE) and ethoxyethyl glycidyl ether (EEGE)) are studied using a metal‐free catalyst system, 3‐phenyl‐1‐propanol (PPA) (an initiator) and 1‐tert‐butyl‐4,4,4‐tris(dimethylamino)‐2,2‐bis[tris‐(dimethylamino)phosphoranylidenamino]‐2Λ⁵,4Λ⁵‐catenadi(phosphazene) (t‐Bu‐P₄) (a promoter) in order to obtain well‐defined functional linear polyethers and diblock copolymers. With the aid of the catalyst system, AGE is found to successfully undergo anionic ring‐opening polymerization (ROP) even at room temperature (low reaction temperature) without any side reactions, producing well‐defined linear AGE‐homopolymer in a unimodal narrow molecular weight distribution. Under the same conditions, EEGE also undergoes polymerization, producing a linear EEGE‐homopolymer in a unimodal narrow molecular‐weight distribution. In this case, however, a side reaction (i.e., chain‐transfer reaction) is found to occur at low levels during the early stages of polymerization. The chemical properties of the monomers in the context of the homopolymerization reactions are considered in the design of a protocol used to synthesize well‐defined linear diblock copolyethers with a variety of compositions. The approach, anionic polymerization via the sequential step feed of AGE and EEGE as the first and second monomers, is found to be free from side reactions at room temperature. Each block of the obtained linear diblock copolymers undergoes selective deprotection to permit further chemical modification for selective functionalization. In addition, thermal properties and structures of the polymers and their post‐modification products are examined. Overall, this study demonstrates that a low‐temperature metal‐free anionic ROP using the PPA/t‐Bu‐P₄ catalyst system is suitable for the production of well‐defined linear AGE‐homopolymers and their diblock copolymers with the EEGE monomer, which are versatile and selectively functionalizable linear aliphatic polyether platforms for a variety of post‐modifications, nanostructures, and their applications.     

Modeling of spiking analog neural circuits using organic semiconductor thin film transistors with silicon oxide nitride semiconductor gates

This paper uses the results of the characterization of amorphous semiconductor thin film transistors (TFTs) with the quasi-permanent memory structure referred to as silicon oxide nitride semiconductor (SONOS) gates, to model spiking neural circuits. SONOS gates were fabricated and characterized. In addition, MOSFETs using organic copper phthalocyanine (CuPc) were fabricated with these SONOS gates to demonstrate proof of concept performance. Analog spiking circuits were then modeled using these low performance TFTs to demonstrate the general suitability of organic TFTs in neural circuits. The basic circuit utilizes a standard comparator with charge and discharge circuits. A simple Hebbian learning circuit was added to charge and discharge the SONOS device. The use of these elements allows for the design and fabrication of high-density 3-dimensional circuits that can achieve the interconnect density of biological neural systems.     

Effects of PAI on interface properties between HfSiO gate dielectric and silicon substrate

The effects of preamorphization implantation (PAI) on the interface properties between hafnium-silicate (HfSiO) gate dielectrics and silicon substrates were examined. In the case of an NH/sub 3/ nitrided interface, it was found that the PAI can improve the interface trap density (D/sub IT/) compared with the no PAI case. However, for the PAI samples, it was also found that samples with sacrificial screening oxide (Sac Ox) had worse interface properties compared with the samples without Sac Ox. It is attributed to the recoiled oxygen from Sac Ox during PAI.     

Survey on blockchain-based non-fungible tokens: History,technologies, standards,and open challenges

This paper presents a survey of non-fungible tokens (NFTs), including its history, technologies, standards, and challenges in their development. An NFT is a unique digital entity that is created and maintained using blockchain technology. Each NFT is identified using a unique smart contract and a token ID, so the whole history of the NFT can be globally identified by its address and token ID. The blockchain information indelibly identifies the current owner of any asset, previous owners, and original creator. NFTs are used to manage ownership of digital and physical assets and cryptocurrencies. The prices of popular NFTs have become very high, and the market for them has overheated in recent years. NFT technology and its ecosystem have evolved since Quantum, the first NFT, was stored in the Namecoin blockchain. Ethereum has become the main platform for NFT projects because it provides support for smart contracts. Currently, almost all NFT projects are launched on the Ethereum blockchain. NFT has two major standards called ERC-721 and ERC-1155, which have had important functions in the development of NFT. Starting with these two standards, other standards for NFT continue to emerge; they expand the functionality of NFT such as by adding utility. However, NFT is a very early technology, and it has not been long after the NFT concept was created and used. So there are several challenges for further improving NFT technology, in terms of usability, interoperability, and evolution. This paper presents a survey of NFT, including its history, technologies, standards, and challenges of NFT.     

Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features

A computer-aided diagnosis (CAD) algorithm identifying breast nodule malignancy using multiple ultrasonography (US) features and artificial neural network (ANN) classifier was developed from a database of 584 histologically confirmed cases containing 300 benign and 284 malignant breast nodules. The features determining whether a breast nodule is benign or malignant were extracted from US images through digital image processing with a relatively simple segmentation algorithm applied to the manually preselected region of interest. An ANN then distinguished malignant nodules in US images based on five morphological features representing the shape, edge characteristics, and darkness of a nodule. The structure of ANN was selected using k-fold cross-validation method with k = 10. The ANN trained with randomly selected half of breast nodule images showed the normalized area under the receiver operating characteristic curve of 0.95. With the trained ANN, 53.3% of biopsies on benign nodules can be avoided with 99.3% sensitivity. Performance of the developed classifier was reexamined with new US mass images in the generalized patient population of total 266 (167 benign and 99 malignant) cases. The developed CAD algorithm has the potential to increase the specificity of US for characterization of breast lesions.     

Monolithic SiGe HBT Feedforward Variable Gain Amplifiers for 5 GHz Applications

Monolithic SiGe heterojunction bipolar transistor (HBT) variable gain amplifiers (VGAs) with a feedforward configuration have been newly developed for 5 GHz applications. Two types of the feedforward VGAs have been made: one using a coupled‐emitter resistor and the other using an HBT‐based current source. At 5.2 GHz, both of the VGAs achieve a dynamic gain‐control range of 23 dB with a control‐voltage range from 0.4 to 2.6 V. The gain‐tuning sensitivity is 90 mV/dB. At V_CTRL= 2.4 V, the 1 dB compression output power, P_1‐dB, and dc bias current are 0 dBm and 59 mA in a VGA with an emitter resistor and ‐1.8 dBm and 71mA in a VGA with a constant current source, respectively.     

Architectural Design Issues in a Clockless 32‐Bit Processor Using an Asynchronous HDL

As technology evolves into the deep submicron level, synchronous circuit designs based on a single global clock have incurred problems in such areas as timing closure and power consumption. An asynchronous circuit design methodology is one of the strong candidates to solve such problems. To verify the feasibility and efficiency of a large‐scale asynchronous circuit, we design a fully clockless 32‐bit processor. We model the processor using an asynchronous HDL and synthesize it using a tool specialized for asynchronous circuits with a top‐down design approach. In this paper, two microarchitectures, basic and enhanced, are explored. The results from a pre‐layout simulation utilizing 0.13‐μm CMOS technology show that the performance and power consumption of the enhanced microarchitecture are respectively improved by 109% and 30% with respect to the basic architecture. Furthermore, the measured power efficiency is about 238 μW/MHz and is comparable to that of a synchronous counterpart.     

Analysis of Failure in Miniature X‐ray Tubes with Gated Carbon Nanotube Field Emitters

We correlate the failure in miniature X‐ray tubes with the field emission gate leakage current of gated carbon nanotube emitters. The miniature X‐ray tube, even with a small gate leakage current, exhibits an induced voltage on the gate electrode by the anode bias voltage, resulting in a very unstable operation and finally a failure. The induced gate voltage is apparently caused by charging at the insulating spacer of the miniature X‐ray tube through the gate leakage current of the field emission. The gate leakage current could be a criterion for the successful fabrication of miniature X‐ray tubes.