Currently, the semiconductor manufacturing industry is seeing rapid movement from 2D planar to 3D FinFET technology. Among SCE-enhanced scaled fin structures, depending on stress engineering to increase mobility, merged elevated source-drain (eSD) epi structures are widely used because they can maximize device performance by reducing Rsd. While there is active research on device and epi own defects related to eSD process, there is no study on yield effect. Smart manufacturing (SM) applications, which form the core of Industry 4.0, are difficult to find in bulk-FinFETs, and it is difficult to find hidden systematic defects of complex three-dimensional structures using limited analyses such as in-line monitoring and abnormal trend detection. In this study, we investigate the root-cause of gate to eSD short, which is the primary FinFET yield detractor, and we obtain an optimized solution to improve yield by 25.2% without performance degradation. These improvements are accomplished using our in-house SM platform that consists of four components: a virtual integration (VI) module for defining defects such as physical connection, void, and not open; a hot spot module for identifying the location of needed process control; an advanced analytics module including algorithms for selecting key features and predicting the fail bit; and an optimizer module that can co-optimize yield and performance.
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http://dx.doi.org/10.1166/jnn.2021.18903 | DOI Listing |
ACS Nano
January 2025
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia.
Implantable systems with chronic stability, high sensing performance, and extensive spatial-temporal resolution are a growing focus for monitoring and treating several diseases such as epilepsy, Parkinson's disease, chronic pain, and cardiac arrhythmias. These systems demand exceptional bendability, scalable size, durable electrode materials, and well-encapsulated metal interconnects. However, existing chronic implantable bioelectronic systems largely rely on materials prone to corrosion in biofluids, such as silicon nanomembranes or metals.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Shenyang 110819, China.
Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet.
View Article and Find Full Text PDFNat Commun
January 2025
School of Environmental Science and Technology, Dalian University of Technology, Dalian, China.
Efficient detection methods are needed to monitor nitrogen dioxide (NO), a major NO pollutant from fossil fuel combustion that poses significant threats to both ecology and human health. Current NO detection technologies face limitations in stability and selectivity. Here, we present a transition metal nitride sensor that exhibits exceptional selectivity for NO, demonstrating a sensitivity 30 times greater than that of the strongest interfering gas, NO.
View Article and Find Full Text PDF3D Print Addit Manuf
December 2024
Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
Microfluidic channel systems can be used for various biomedical applications, including drug administration, wound healing, cell culture research, and many others. A 3D microfluidic channel system has enormous potential over conventional microfluidic channel systems, including the capacity to simulate biological events in a laboratory setting. This system has the ability to recreate biological phenomena such as concentration gradient generators (CGGs).
View Article and Find Full Text PDFSmall
January 2025
State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou, 730000, China.
Smart hydrogel sensors with intrinsic responsiveness, such as pH, temperature, humidity, and other external stimuli, possess broad applications in innumerable fields such as biomedical diagnosis, environmental monitoring, and wearable electronics. However, it remains a great challenge to develop wearable structural hydrogels that possess simultaneously body temperature-responsive, adhesion-adaptable, and transparency-tunable. Herein, an innovative skin-mountable thermo-responsive hydrogel is fabricated, which endows tunable optical properties and switchable adhesion properties at different temperatures.
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