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In this letter, a new method based on the temperature dependence of gate leakage current for accurately characterizing thermal crosstalk is proposed and performed on the advanced silicon-on-insulator (SOI) MOSFETs. The developed technique enables to capture the pure thermal crosstalk effect as it excludes the self-heating impact during the measurement. Moreover, it is proven robust over a large range of gate voltages regardless of the difference of device types, which is demonstrated on a pair of devices with a shared port. The accuracy of the thermal crosstalk measurement benefits in determining the overall thermal performance of the nano-scaled circuits under the mutual heating impacts. It offers a possible hardware solution to thermal monitor and management.

A new method using temperature dependent shared series resistance in a pair of devices with common drain configuration is proposed to measure self-heating (SH) in advanced MOSFETs. The proposed measurement technique depends on the intrinsic series resistance and is not limited to the spatial proximity between the heater and the sensor. The developed method demonstrated on the silicon-on-insulator (SOI) MOSFETs reveals that the more accurate SH assessment can be obtained compared to other methods that are sensitive to the distance from heat source. Furthermore, the extraction of intrinsic series resistance allows to quantify the process-induced variability of the contact resistance.

As a promising new generation of nonvolatile memory, HfO2-based resistive random-access memory (RRAM) has attracted extensive research. However, the problem of data retention has prevented its industrial production as embedded memory. In this paper, from the microscopic understanding, a Monte Carlo simulator is developed to investigate the effects of an oxygen reservoir layer (ORL) on the resistance instability of HfO2-based RRAM. The evolution of conductive filaments (CF) during the retention degradation is visualized by our simulation considering the physical mechanisms of oxygen ions absorbed and released by the ORL. The simulation results are further validated with experiments to provide the prediction of retention performance of RRAMs with different ORLs and operation schemes.

An efficient variability- and reliability-aware methodology is proposed to achieve the device-circuit co-design and is demonstrated on Nanosheet (NS) FETs based circuits. The main features include: (i) The single trap impact mapping (STIM) method links the charge distributions to the time-dependent variability and reliability. (ii) A trap behavior-based 3D-Kinetic Monte Carlo simulator is developed to capture the statistical charge distributions in the gate dielectric under different stress conditions in the presence of time-zero variations. (iii) A database which includes ΔV th , ΔSS and its correlation under various device degradation probabilities (DP) and thermal stress ratios (TSR) is set up, significantly reducing the device simulation time. (iv) A variation-aware model based on the database is proposed to predict the time-dependent delay degradation and potential critical paths in the digital circuits coupled with self-heating at arbitrary operating conditions.

With technology node scaling down to 5 nm, the narrow device geometry confines the material thermal conductivity and further aggravates the self-heating effect in gate-all-around (GAA) transistors. In this paper, we investigate the self-heating of horizontally stacked three-layer GAA nanosheet transistors by 3-D finite-element modeling (FEM) simulation. The anisotropic thermal conductivity of nanosheets with the dependence of silicon thickness and temperature is implemented in the FEM simulator to evaluate thermal behavior accurately. The impact of layout design on thermal properties is investigated comprehensively from single device to device arrays with implication on electrical performance. The results indicate that the width of nanosheet is the key parameter to make the tradeoffs between self-heating and electrical characteristic. Meanwhile, the optimizations of layout design are given to suppress the thermal effects, including self-heating, nonuniformity of temperature, and thermal crosstalk at device level. This paper will provide guidelines for layout design, thermal management, device performance, and thermal-aware reliability prediction in the GAA-stacked structure.

Salidroside (Sal) is a natural antioxidant that elicits cardioprotective and neuroprotective effects in vivo and in vitro; however, its impact on hepatic ischemia/reperfusion (I/R) injury remains unclear. The purpose of this study was to investigate the hepatoprotective effects of salidroside against segmental (70%) warm hepatic I/R injury in rats. Animals were randomized into Sham, Sham+salidroside pretreatment (Sal), Sham+Sal+carboxyatractyloside (CATR), Sham+CATR, I/R, I/R+Sal, I/R+Sal+CATR and I/R+CATR groups. The hepatic artery, left portal vein and median liver lobes were occluded for 60min and then unclamped to allow reperfusion. Pretreatment with salidroside (20mg/kg/day for 7 days, intraperitoneally) significantly decreased serum alanine aminotransferase (sALT) and serum aspartate aminotransferase (sAST) levels after 6h and 24h of reperfusion and protected the liver against I/R-induced injury. However, this protective effect could be reversed by CATR, a mitochondrial permeability transition pore (MPTP) opener (5mg/kg 30min before I/R insult, intraperitoneally). Mechanistic studies have revealed that salidroside inhibits glycogen synthase kinase-3 beta (GSK-3β) activity and enhances the NF-E2-related factor (Nrf2)-dependent antioxidant response by activating the Akt signaling pathway, thereby reducing mitochondrial reactive oxygen species generation, increasing MPTP resistance and preventing apoptosis by suppressing cytochrome c release and caspase activation during reperfusion. Therefore, salidroside ameliorates hepatocyte death and apoptosis through activation of the GSK-3β/Nrf2-dependent antioxidant response and subsequent MPTP inhibition. These results provide experimental evidence supporting the clinical use of salidroside for hepatoprotection in surgical settings.

Non-planar device structures such as FinFETs are widely used in advanced CMOS technology, the non-planar structures with confined narrow channel region and the low thermal conductivity of materials will prevent heat dissipation, resulting in a localized lattice temperature rise, named self-heating effect (SHE) [1]–[3]. The SHE not only causes transistors' and circuits' performance varability and degradation but also exacerbates degradation of transistors' reliability and leading to the reduction of circuit's lifetime, which becomes a significant challenge to device engineers and circuit designers [4]–[6]. Thus, the self-heating effect is one of the important issues in the semiconductor industry. For device optimization and circuit design, it is necessary to capture self-heating induced statistical variability and reliability during circuit operation. Normally, the worst-case analysis for SHE at device and circuit level is used to provide a thermal-aware design guide, however, it may cause an inaccurate estimation as self-heating can be different in actual circuit operation.

In this work, multi-layer-oxide based RRAM devices with the structure of TiN/(HfO 2 /Al 2 O 3 ) 8 /Ti/TiN are designed and fabricated, which are compatible with CMOS technology. Two operation modes are proposed to achieve different resistive switching characteristics in the multi-layer-oxide based RRAM devices. In the operation mode I, low-power switching characteristics with 0.11uW set power (−100nA at −1.14V) and 0.49uW reset power (0.27uA at 1.79V) are obtained. In the operation mode II, multilevel storage with seven stable resistance states is obtained, and analog characteristic with continuously adjustable resistance is demonstrated. The possible switching mechanisms are discussed.

In this paper a new methodology is proposed to investigate variability and reliability correlated with self-heating effect (SHE) in digital circuits during random operation. In this methodology, the arbitrary power waveform (APW) self-heating model is applied to carry out self-heating evaluation with the input sequences generated by the power waveform generator (PWG). Based on the proposed method, self-heating induced variability and HCI degradation in Nanosheet-FETs based SRAM are investigated. The results show it is essential to take the self-heating variation into account for circuit design and reliability prediction.

The self-heating behavior of horizontally stacked gate-all-around (GAA) nanosheet transistor is evaluated to investigate the spatial temperature profile and heat flux distribution considering the simple back-end-of line (BEOL). The impacts of related device geometry and material thermal conductivity are given to provide guidelines for mitigating self-heating effect (SHE) in device design. The results indicate that self-heating in nanoscale device should be attached great importance in achieving robust thermal management and precise reliability prediction.

Nowadays, there is an increasing demand for the identification of an organization's intellectual capital (IC) for decision support and providing important managerial insights in knowledge-intensive industries. In traditional approaches, identification of an organization's IC is usually done manually through interviews, surveys, workshops, etc. These methods are labor and time intensive and the quality of the results is highly dependent on, among other things, the experience of the investigators. This paper presents a Knowledge-based Intellectual Capital Extraction (KBICE) algorithm which incorporates the technologies of computational linguistics and artificial intelligence (AI) for automatic processing of unstructured data and extraction of important IC-related information. The performance of KBICE was assessed through a series of experiments conducted by using publicly available financial reports from the banking industry as the testing batch and encouraging results have been obtained. The results showed that, through the use of hybrid intelligent matching strategies, it is possible to extract commonly referred IC-related information from unstructured data automatically. IC information analyst can rely on this method as an additional mean to identify and extract the commonly sought IC information from financial reports in a fast, systematic and reliable manner.

The mechanical strengths of ABS (Acrylonitrile Butadiene Styrene) components fabricated by fused deposition modeling (FDM) technique have been studied, with the focus on the effect of printing orientations on the strength. Using the properties derived from stress-strain curves of the samples, the 0-degree printed sample has the strongest mechanical properties, which is likely due to preferred orientations in individual slice.

FGF10 is a member of fibroblast growth factors (FGFs). We previously showed that FGF10 protects neuron against oxygen-glucose deprivation injury in vitro; however, the effect of FGF10 in ischemic stroke in vivo is unknown. In the present study, we showed that FGF10 was mainly expressed in neurons but not astrocytes and detected FGF10 in mouse cerebrospinal fluid. The FGF10 levels in neurons culture medium and cell lysate were much higher than those in astrocytes. FGF10 expression in brain tissue and FGF10 level in CSF were increased in mouse middle cerebral artery occlusion (MCAO) model. Administration of FGF10 into lateral cerebroventricle not only decreased MCAO-induced brain infarct volume and neurological deficit, but also reduced the number of TUNEL-positive cells and activities of Caspases. Moreover, FGF10 treatment depressed the triggered inflammatory factors (TNF-α and IL-6) and NF-κB signaling pathway and increased phosphorylation of PI3K/Akt signaling pathway. Blockade of PI3K/Akt signaling pathway by wortmannin and Akt1/2-kinase inhibitor, partly compromised the neuroprotection of FGF10. However, blockade of PI3K/Akt signaling pathway did not impair the anti-inflammation action of FGF10. Collectively, our results demonstrate that neuron-derived FGF10 ameliorates cerebral ischemia injury via inhibiting NF-κB-dependent neuroinflammation and activating PI3K/Akt survival signaling pathway in mice.

Purpose – The purpose of this paper is to build on Dumay and Cai’s (2014) prior research to provide a deeper analysis of the problems associated with using content analysis (CA) as a research methodology for investigating intellectual capital disclosure (ICD). Design/methodology/approach – Totally, 110 articles utilising CA as a research methodology for inquiring into ICD are analysed based on Krippendorff’s (2013) conceptual CA research framework and design logic, and tied into issues relating to CA as a research methodology for investigating ICD. Findings – The authors advocate that ICD CA researchers need to go back to the drawing board and ensure that future studies rigorously apply the basic logic of CA design. In its current state, ICD CA research needs to take a few steps back, before it can move forward. If ICD CA researchers can accomplish this, then there is an opportunity to undertake rigorous research to develop reliable and valid outputs that add to new knowledge about IC. Research limitations/implications – The main limitations of the research are the chosen sample of CA-based ICD articles and the adoption of the Krippendorff’s framework. However, the authors have identified the main corpus of CA-based ICD studies and since Krippendorff is the only recognised comprehensive text on CA as a methodology, the authors use the most appropriate data and framework possible for the analysis. Originality/value – Prior CA studies have laid the foundation for what is a popular research methodology. However, the authors argue that the popularity of CA as a research method for investigating ICD has become so great that at times the research methodology “drives the research questions” as opposed to the “research questions driving the methodology” Hence, this research examines reasons for CA limited contemporary contribution and recommends how this may be overcome rather than prescribing how to conduct ICD CA research.

As an important tool in terms of value creation, intellectual capital (IC) is getting the increasing attention about its role in organization management. However, the inadequate technique of identifying IC is still a big challenge for organizations in visualizing the complexity of IC. According to the latest development in IC, this paper presents an intelligent method to extract IC from the massive amount of data materials. The adopted method is via a pre-constructed an IC taxonomy and an intelligent tool to extract the IC-related information and classify the information into good, bad or neutral.

Integration and interoperability is a basic requirement for geographic information systems (GIS) The web provides access to geographic data in several ways: on the one hand, web-based interactive GIS applications provide maps and routing information to end users; on the other hand, the data of some GIS can be accessed in a programmatic way using a web service Thereby, the data is made available for other GIS applications However, integrating data from various sources is a tedious task which requires the mapping of the involved schemas as a first step Schema matching analyzes and identifies similarities of two schemas, but all approaches can be only semi-automatic as human intervention is required to verify the result of a schema matching algorithm In this paper, we present an approach that improves the matching result of existing solutions by using semantic information provided by the context of the geographic application This reduces the effort for manually correcting the results which has been validated in several application examples.