Zinc sulfide [ZnS] thin films were deposited on glass substrates using radio frequency magnetron sputtering. The substrate temperature was varied in the range of 100°C to 400°C. The structural and optical properties of ZnS thin films were characterized with X-ray diffraction [XRD], field emission scanning electron microscopy [FESEM], energy dispersive analysis of X-rays and UV-visible transmission spectra. The XRD analyses indicate that ZnS films have zinc blende structures with (111) preferential orientation, whereas the diffraction patterns sharpen with the increase in substrate temperatures. The FESEM data also reveal that the films have nano-size grains with a grain size of approximately 69 nm. The films grown at 350°C exhibit a relatively high transmittance of 80% in the visible region, with an energy band gap of 3.79 eV. These results show that ZnS films are suitable for use as the buffer layer of the Cu(In, Ga)Se2 solar cells.
The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping.
In this study, we investigated the structural and optical properties of indium sulfide (In2S3) thin films as a substitute for the CdS buffer layer in Cu(In,Ga)Se2 (CIGS) solar cells. The In2S3 films were deposited on glass substrates using radio frequency (RF) magnetron sputtering. The sputtering power was changed from 60 to 120 W in 20 W increments. The effects of sputtering power on the crystallinity, surface morphology, and optical properties of the films were characterized with X-ray diffraction (XRD), atomic force microscopy (AFM), energy dispersive X-ray spectroscopy (EDS), and UV-visible spectrophotometry. The XRD analyses indicated that the films were polycrystalline β-In2S3 structures with two preferred orientations along the (103) and (206) directions. The AFM images revealed that the films had nanosized grains and that the size increased from 7 nm for the samples prepared at 60 W to 13 nm for those prepared at 120 W. The optical band gap of the samples was found to vary between 2.88 and 2.43 eV.
Artificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial intelligence accelerators. In this paper, we propose the ASimOV framework, which optimizes artificial intelligence algorithms and generates Verilog hardware description language (HDL) code for executing intelligence algorithms in field programmable gate array (FPGA). To verify ASimOV, we explore the performance space of k-NN algorithms and generate Verilog HDL code to demonstrate the k-NN accelerator in FPGA. Our contribution is to provide the artificial intelligence algorithm as an end-to-end pipeline and ensure that it is optimized to a specific dataset through simulation, and an artificial intelligence accelerator is generated in the end.
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