Intel Moore observed an exponential doubling in the number of transistors in every 18 months through the size reduction of transistor components since 1965. In viewing of mobile computing with insatiate appetite, we explored the necessary enhancement by an increasingly maturing nanotechnology and facing the inevitable quantum-mechanical atomic and nuclei limits. Since we cannot break down the atomic size barrier, the fact implies a fundamental size limit at the atomic/nucleus scale. This means, no more simple 18-month doubling, but other forms of transistor doubling may happen at a different slope. We are particularly interested in the nano enhancement area. (i) 3 Dimensions: If the progress in shrinking the in-plane dimensions is to slow down, vertical integration can help increasing the areal device transistor density. As the devices continue to shrink into the 20 to 30 nm range, the consideration of thermal properties and transport in such devices becomes increasingly important. (ii) Quantum computing: The other types of transistor material are rapidly developed in laboratories worldwide, for example, Spintronics, Nanostorage, HP display Nanotechnology, which are modifying this Law. We shall consider the limitation of phonon engineering fundamental information unit "Qubyte" in quantum computing, Nano/Micro Electrical Mechanical System (NEMS), Carbon Nanotubes, singlelayer Graphenes, single-strip Nano-Ribbons, and so forth.

Introduction With the development of nanofabrication technologies, an increasing effort has been placed on plasmonic sensors [1,2]. Plasmonic sensors are based on surface plasmon resonance (SPR) or localized surface plasmon resonance (LSPR) [1-3]. When the target analytes bond to the functionalized material and altered the local refractive index, the resonance wavelength shifts due to the change of the local refractive index. Many types of plasmonic sensors have been studied and developed for chemical sensing or biochemical sensing applications in the last decade. There are two main challenges for chemical and biochemical sensing, i.e., sensitivity and specificity, especially when working in sensing applications in a complex background such as environmental monitoring, breath analysis, hazard tracing, etc.[1,3,4]. Besides, the development of artificial intelligence and machine learning technology provides the feasibility to correlate sensor responses to multiple parameters[4]. In this work, we report a novel plasmonic sensing platform with enhanced sensitivity and an AI-assisted signal processing algorithm to improve the specificity of plasmonic sensors. Sensor Simulation To achieve higher sensitivity and a broader range of detection. We have theoretically studied and simulated a sensing platform, namely, nano plasmonic pillars (NPP) (Figure 1). SiO2 nanopillars with a diameter of 100 nm are coated with a 20 nm-thick gold layer. The nanopillars are hexagonally displaced with a displacement of 300 nm. The Au-coated nanopillar has an interface between metal (Au) and insulators (SiO2) along the nanopillars' whole surface area. This structure will generate localized surface plasmon resonance (LSPR) along the nanopillars and improve the sensitivity due to the large effective surface area. The parameters, i.e., size, material, and displacement of the nanopillars, are simulated and optimized using finite element analysis. Figure 2 compares the simulated wavelength shift of the Au-coated SiO2 pillars and the ones of solid Au pillars when the local refractive index changes from 1.0 to 1.05. It is shown that the sensitivity (slope of the fitted function) of the simulated design is 2.90 times higher than the solid pillar. AI-Assisted Signal Processing One advantage of plasmonic sensors is that the sensor response can be captured by CMOS cameras. In this way, the wavelength shift caused by the analytes can be monitored as a color change of the captured image. With the rapid development of machine learning technology, convolutional neural networks have become a robust tool to help process the sensor signals. In this work, we developed a convolutional neural network-based algorithm that can successfully predict the local refractive index based on the simulated wavelength. Figure 3 shows the signal processing method applied in this work. First, the transmission spectra of the NPP with refractive indices of 1.0, 1.01, 1.02, 1.03, 1.04, and 1.05 are simulated. The spectra have dual peaks located in the visible wavelength due to the specifically designed plasmonic features (Figure 1). Second, approximate RGB values are calculated based on the spectra following Plank's law. A random integer (from -3 to 3) was added to the calculated RGB as random noise. Over 5,000 images (64x64 pixels each) for each corresponding spectrum (30,000 images in total) were generated based on the RGB value and the random noises. Finally, the images were trained and tested by the developed convolutional neural network. We successfully predicted the local refractive index with 99% accuracy with 5 cross-validations. Note: we use the simulated images to prove the concept in this work. The same method can be applied when detecting real analytes with proper adjustment. We believe the reported work can help to improve both sensitivity and selectivity in chemical and biochemical sensing. References [1] Soler, M., Huertas, C. S., & Lechuga, L. M. (2019). Label-free plasmonic biosensors for point-of-care diagnostics: a review. Expert review of molecular diagnostics, 19(1), 71-81. [2] Xu, Y., Bai, P., Zhou, X., Akimov, Y., Png, C. E., Ang, L. K., ... & Wu, L. (2019). Optical refractive index sensors with plasmonic and photonic structures: promising and inconvenient truth. Advanced Optical Materials, 7(9), 1801433.. [3] Zhao, Y., Mukherjee, K., Benkstein, K. D., Sun, L., Steffens, K. L., Montgomery, C. B., ... & Zaghloul, M. E. (2019). Miniaturized nanohole array based plasmonic sensor for the detection of acetone and ethanol with insights into the kinetics of adsorptive plasmonic sensing. Nanoscale, 11(24), 11922-11932. [4] Feng, S., Farha, F., Li, Q., Wan, Y., Xu, Y., Zhang, T., & Ning, H. (2019). Review on smart gas sensing technology. Sensors, 19(17), 3760. Figure 1

We demonstrate that photoemission properties of GaAs photocathodes (PCs) can be altered by surface acoustic waves (SAWs) generated on the PC surface due to dynamical piezoelectric fields of SAWs. Simulations with COMSOL indicate that electron effective lifetime in p-doped GaAs may increase by a factor of 10x to 20x. It implies a significant, by a factor of 2x to 3x, increase of quantum efficiency (QE) for GaAs PCs. Essential steps in device fabrication are demonstrated, including deposition of an additional layer of ZnO for piezoelectric effect enhancement, measurements of I-V characteristic of the SAW device, and ability to survive high-temperature annealing.

Sensing biomarkers in exhaled breath offers a potentially portable, cost-effective, and noninvasive strategy for disease diagnosis screening and monitoring, while high sensitivity, wide sensing range, and target specificity are critical challenges. We demonstrate a deep learning-assisted plasmonic sensing platform that can detect and quantify gas-phase biomarkers in breath-related backgrounds of varying complexity. The sensing interface consisted of Au/SiO2 nanopillars covered with a 15 nm metal–organic framework. A small camera was utilized to capture the plasmonic sensing responses as images, which were subjected to deep learning signal processing. The approach has been demonstrated at a classification accuracy of 95 to 98% for the diabetic ketosis marker acetone within a concentration range of 0.5–80 μmol/mol. The reported work provides a thorough exploration of single-sensor capabilities and sets the basis for more advanced utilization of artificial intelligence in sensing applications.