Improved Physical Design for Manufacturing Awareness and Advanced VLSI

Increasing challenges arise with each new semiconductor technology node, especially in advanced nodes, where the industry tries to extract every ounce of benefit as it approaches the limits of physics, through manufacturing-aware design technology co-optimization and design-based equivalent scaling. The increasing complexity of design and process technologies, and ever-more complex design rules, also become hurdles for academic researchers, separating academic researchers from the most up-to-date technical issues.This thesis presents innovative methodologies and optimizations to address the above challenges. There are three directions in this thesis: (i) manufacturing-aware design technology co-optimization; (ii) advanced node design-based equivalent scaling; and (iii) an open source academic detailed routing flow.To realize manufacturing-aware design technology co-optimization, this thesis presents two works: (i) a multi-row detailed placement optimization for neighbor diffusion effect mitigation between neighboring standard cells; and (ii) a post-routing optimization to generate 2D block mask layout for dummy segment removal in self-aligned multiple patterning.To achieve advanced node design-based equivalent scaling, this thesis presents two improved physical design methodologies: (i) a post-placement flop tray generation approach for clock power reduction; and (ii) a detailed placement approach to exploit inter-row M1 routing for congestion and wirelength reduction.To address the increasing gap between academia and industry, this thesis presents two works toward an open source academic detailed routing flow: (i) a complete, robust, scalable and design ruleaware dynamic programming-based pin access analysis framework; and (ii) TritonRoute – the open source detailed router that is capable of delivering DRC-clean detailed routing solutions in advanced nodes.This thesis concludes with a summary of its contributions and open directions for future research

High‐Performance Pressure Sensors Based on Shaped Gel Droplet Arrays

Polymer gel-based pressure sensors offer numerous advantages over traditional sensing technologies, including excellent conformability and integration into wearable devices. However, challenges persist in terms of their performance and manufacturing technology. In this study, a method for fabricating gel pressure sensors using a hydrophobic/hydrophilic patterned surface is introduced. By shaping and fine-tuning the droplets of the polymer gel prepolymerization solution on the patterned surface, remarkable sensitivity improvements compared to unshaped hydrogels have been achieved. This also showcased the potential for tailoring gel pressure sensors to different applications. By optimizing the configuration of the sensor array, an uneven conductive gel array is fabricated, which exhibited a high sensitivity of 0.29 kPa$^{−1}$ in the pressure range of 0–30 kPa, while maintaining a sensitivity of 0.13 kPa$^{−1}$ from 30 kPa up to 100 kPa. Furthermore, the feasibility of using these sensors for human motion monitoring is explored and a conductive gel array for 2D force detection is successfully developed. This efficient and scalable fabrication method holds promise for advancing pressure sensor technology and offers exciting prospects for various industries and research fields

Broad-Wavevector Spin Pumping of Flat-Band Magnons

We report the experimental observation of large spin pumping signals in YIG/Pt system driven by broad-wavevector spin-wave spin current. 280 nm-wide microwave inductive antennas offer broad-wavevector excitation which, in combination with quasi-flatband of YIG, allows a large number of magnons to participate in spin pumping at a given frequency. Through comparison with ferromagnetic resonance spin pumping, we attribute the enhancement of the spin current to the multichromatic magnons. The high efficiency of spin current generation enables us to uncover nontrivial propagating properties in ultra-low power regions. Additionally, our study achieves the spatially separated detection of magnons, allowing the direct extraction of the decay length. The synergistic combination of the capability of broad-wavevector excitation, enhanced voltage signals, and nonlocal detection provides a new avenue for the electrical exploration of spin waves dynamics

Validation of the digital health literacy assessment among the university students in China

PurposeWith the development of the internet, digital health literacy (DHL) has become increasingly important for managing health. Consequently, various digital health literacy scales have been created for different groups. The purpose of this study was to verify the reliability and validity of the simplified Chinese version of the Digital Health Literacy Assessment (DHLA) scale among university students in China.MethodSnowball sampling was used to recruit the participants via an online platform (Wenjuan.com), and finally 304 university students were included in the survey. Demographic information and the status of DHL were collected through the online questionnaire. Cronbach’s alpha and split-half reliability were used to test the internal consistency of the scale, while the structural validity was verified by exploratory factor analysis and confirmatory factor analysis. Additionally, the convergence of the scale was tested by composite reliability (CR) and average variance extracted (AVE).ResultTwo dimensions were generated from 10 entries in the scale, named Self-rated Digital Health Literacy and Trust Degree of Online Health Information, respectively. The Cronbach’s alpha and split-half reliability of the total scale were 0.912 and 0.828, while the Cronbach’s alpha of the two dimensions were 0.913 and 0.830, respectively. The structural validity-related indexes of the scale met the standards (RMSEA = 0.079, GFI = 0.943, AGFI = 0.902, CFI = 0.971). In each dimension, the CR and AVE also reached critical values (CR > 0.7 and AVE > 0.5).ConclusionThe scale had high reliability and validity, indicating the simplified Chinese DHLA scale could be used to evaluate the DHL of university students in China

Nonlocal Detection of Interlayer Three-Magnon Coupling

A leading nonlinear effect in magnonics is the interaction that splits a high-frequency magnon into two low-frequency magnons with conserved linear momentum. Here, we report experimental observation of nonlocal three-magnon scattering between spatially separated magnetic systems, viz. a CoFeB nanowire and a yttrium iron garnet (YIG) thin film. Above a certain threshold power of an applied microwave field, a CoFeB Kittel magnon splits into a pair of counterpropagating YIG magnons that induce voltage signals in Pt electrodes on each side, in excellent agreement with model calculations based on the interlayer dipolar interaction. The excited YIG magnon pairs reside mainly in the first excited (n=1) perpendicular standing spin-wave mode. With increasing power, the n=1 magnons successively scatter into nodeless (n=0) magnons through a four-magnon process. Our results demonstrate nonlocal detection of two separately propagating magnons emerging from one common source that may enable quantum entanglement between distant magnons for quantum information applications.</p

Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach

Inferring potential adverse drug reactions is an important and challenging task for the drug discovery and healthcare industry. Many previous studies in computational pharmacology have proposed utilizing multi-source drug information to predict drug side effects have and achieved initial success. However, most of the prediction methods mainly rely on direct similarities inferred from drug information and cannot fully utilize the drug information about the impact of protein&ndash;protein interactions (PPI) on potential drug targets. Moreover, most of the methods are designed for specific tasks. In this work, we propose a novel heterogeneous network embedding approach for learning drug representations called SDHINE, which integrates PPI information into drug embeddings and is generic for different adverse drug reaction (ADR) prediction tasks. To integrate heterogeneous drug information and learn drug representations, we first design different meta-path-based proximities to calculate drug similarities, especially target propagation meta-path-based proximity based on PPI network, and then construct a semi-supervised stacking deep neural network model that is jointly optimized by the defined meta-path proximities. Extensive experiments with three state-of-the-art network embedding methods on three ADR prediction tasks demonstrate the effectiveness of the SDHINE model. Furthermore, we compare the drug representations in terms of drug differentiation by mapping the representations into 2D space; the results show that the performance of our approach is superior to that of the comparison methods

Design and application of smart-microgrid in industrial park

Due to the uncertain and randomness of both wind power photovoltaic output of power generation side and charging load of user side, a set of wind-solar-storage-charging multi-energy complementary smart microgrid system in the park is designed. Through AC-DC coupled, green energy, such as wind energy, distributed photovoltaic power and battery echelon utilization energy storage power, can be supplemented as factory power. While alleviating the power consumption pressure in the plant, it also realizes functions such as smoothing the fluctuation green energy power generation, and peak loading shifting. Vehicle DC super and fast charging are also integrated in this station. The system realizes real-time state monitoring of different energy sources, energy storage, power distribution, and loads, which can guarantee green, smooth, efficient and economic operation of the multi-energy complementary system in the plant

Strengthen the bonding of self-glazed zirconia to enamel by sol–gel coating

Sol–gel coating technology was applied to modify the bonding surface of self-glazed zirconia (SZ). Its effect on the bond strength of SZ to enamel was evaluated in vitro in comparison with the conventional milled zirconia treated by airborne-particle abrasion (CZa) and the heat-pressed lithium disilicate glass-ceramics treated by etching and silanization (LDe). All ceramic specimens were bonded to the etched bovine enamel with adhesive resin cement. Shear bond strength of both sol–gel coated SZ groups with different strategies (SZc1 and SZc2) was 20.00 ± 7.07 MPa and 18.32 ± 3.63 MPa, respectively, which was comparable to that of LDe, 18.44 ± 2.27 MPa (p > 0.05) and significantly higher than that of CZa, 11.72 ± 1.48 MPa, and SZ, 3.06 ± 1.66 MPa (p < 0.05). Lots of voids between zirconia clusters were observed on the bonding surface of SZc1, yet that of SZc2 showed homogeneous honeycomb nanoporous structure. All groups exhibited mixed failure except SZ, and the fracture surfaces of SZc1, SZc2, and LDe showed better wettability than CZa. Sol–gel coating could improve the bond strength of SZ to enamel, and the sol–gel coated SZ might have the potential to make minimally invasive restorations