Browsing by Author "Parashar, Parag"

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A path-finding toward high-efficiency penternary Cu(In,Ga)(Se,S)(2) thin film solar module
(2019-01-01)
Huang, Chien-Yao
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Parashar, Parag
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Chou, Hao-Ming
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Lin, Yi-Shivan
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Lin, Albert
The optimal p-n junction structure in a state-of-the-art Cu(In,Ga)(Se,S)(2) thin-film solar module technology is investigated. For co-optimization design and path-finding, a TCAD model is developed with experimental samples. The engineerable parameters, i.e., F-Ga, GGI(avg), and CdS thickness, are demonstrated to play a critical role in determining the p-n junction properties such as dark current characteristics J(dark)(V), voltage-dependent photocurrent, localized carrier collection efficiency, and interface carrier transportation. We show the optimal Ga-grading is determined by a trade-off between the recombination loss in space charge region and the photo carrier collection in quasi-neutral region. The optimal CdS thickness is determined by a trade-off between carrier collection efficiency, short-circuit current (J(SC)) loss, and J(dark)(V), which depends on varied Ga-profiles. Overall, thin CdS (<= 10 nm) is preferred to reduce the J(SC) loss in accumulated Ga-profiles, while thicker CdS is preferred to enhance the carrier collection efficiency in flatter Ga-profiles. The band alignment effect on varied Cu(In,Ga)(Se,S)(2)/CdS junctions is also investigated. It is found sulfur-incorporation can suppress the V-OC saturation behavior at wide bandgap. For CIGSeS absorber with SS = 20% and D-p = 15%, the maximum V-OC of 780 mV can be achieved by co-optimized Ga-profile. Furthermore, varied Ga-profiles and CdS buffer layers are explored for pathfinding. An optimal p-n junction structure shows a relative + 40% efficiency improvement from 15.5% to 21.9%. This work shows the efficiency headroom of reported CIGSeS thin-film solar module technology through co-optimized CIGSeS composition gradient and buffer layer.
An electrophoretic-deposited low-cost carbon nanotube (CNT) thermophotovoltaic emitter
(2018-01-01)
Lin, Albert
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Parashar, Parag
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Yang, Chien-Chih
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Hao-MingChou
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Lin, Yi-Shiuan
We use electrophoresis technique to deposit a low-cost carbon nanotube (CNT) film on tantalum (Ta) for thermophotovoltaic (TPV) emitters. The initial thermal measurement of the EPD-CNT film from 200 degrees C-800 degrees C is conducted. The elimination of high-temperature CVD deposition facilitates the commercialization of TPV devices. The UV-VIS-NIR spectrum and high-temperature FTIR measurement are reported to assess the feasibility of EPD-CNT as a TPV emitter candidate. The planarized nature of the proposed emitter can be easily integrated with dielectric multi-layer filter for spectral shaping depending on applications.
An ultra-compact blackbody using electrophoretic deposited carbon nanotube films
(2018-01-01)
Lin, Albert
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Yang, Chien-Chih
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Parashar, Parag
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Lin, Chien-Yung
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Jian, Ding Rung
Carbon nanotubes (CNTs) possesses decent optical properties and thus can be considered as a candidate for perfect absorbers due to their close-to-air refractive index and minimal extinction. However, weak absorption in porous materials, due to the low extinction coefficients, requires an inevitably thick absorption layer (similar to 100 mu m) for the perfect opaque absorbers. Thus, the requirement of large thicknesses of CNTs prohibits them from being used as miniaturized integrated photonic devices. Here, we propose an electrophoretic deposited (EPD) CNT resonant cavity structure on tantalum (Ta) to enhance optical absorption. Efficient random light scattering along with the resonant cavity structure using Ti/SiO2 stacking enhances the absorption in our proposed EPD-CNT film while maintaining the total device thickness to <1 mu m. The experiment results reveal that the absorption band covers the entire UV-VIS-NIR spectrum (lambda = 0.3-2.6 mu m), using resonant-cavity EPD-CNT design. The EPD deposition process is done at relatively low temperature < 120 degrees C. We believe that this proposal is very promising for sensing, antenna, and thermophotovoltaics (TPV), in terms of bandwidth, compactness and cost.
Analytics-statistics mixed training and its fitness to semisupervised manufacturing
(2019-08-13)
Parashar, Parag
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Chen, Chun Han
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Akbar, Chandni
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Fu, Sze Ming
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Rawat, Tejender S.
While there have been many studies using machine learning (ML) algorithms to predict process outcomes and device performance in semiconductor manufacturing, the extensively developed technology computer-aided design (TCAD) physical models should play a more significant role in conjunction with ML. While TCAD models have been effective in predicting the trends of experiments, a machine learning statistical model is more capable of predicting the anomalous effects that can be dependent on the chambers, machines, fabrication environment, and specific layouts. In this paper, we use an analytics-statistics mixed training (ASMT) approach using TCAD. Under this method, the TCAD models are incorporated into the machine learning training procedure. The mixed dataset with the experimental and TCAD results improved the prediction in terms of accuracy. With the application of ASMT to the BOSCH process, we show that the mean square error (MSE) can be effectively decreased when the analytics-statistics mixed training (ASMT) scheme is used instead of the classic neural network (NN) used in the baseline study. In this method, statistical induction and analytical deduction can be combined to increase the prediction accuracy of future intelligent semiconductor manufacturing.
Down Shifted Converssion for Enhanceed HIT SSolar Cell Efficiency
(2017-01-01)
Lin, Albert S.
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Parashar, Parag
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Huang, Wei-Ming
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Huang, Yi-Wen
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Jian, Ding-Rung
In this work, HIT solar cell is incorporated with the luminescent down-shifting (LDS) mechanism for the incident solar spectrum. The heterojunction with intrinsic thin-layer(HIT) solar cell is highly efficient, low temperature (200 C), low cost, and highly symmetrical device. Nevertheless, the carrier collection efficiency is low for the short wavelength solar photons due to the surface recombination. Here, a thin phosphor layer is spray coated on the top of HIT solar cells for the luminescent down-shifting of high energy photons and thus enhanced external quantum efficiency (EQE) for the device for lambda=300nm-450nm is observed.
HIT Solar Cell Performance Enhancement with Luminescent Down Shifting Phenomenon
(2018-01-01)
Lin, Albert
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Huang, Chien-Yao
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Parashar, Parag
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Chou, Hao-Ming
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Lin, Yi-Shiuan
Luminescent down-shifting (LDS) phenomenon has been employed to enhance the HIT solar cell efficiency. The efficiency and J(SC) are increased by 2.3% and 11.5% respectively. External quantum efficiency (EQE) data supports our claim.
Intelligent Manufacturing: TCAD-Assisted Adaptive Weighting Neural Networks
(2018-01-01)
Huang, Chien Y.
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Fu, Sze M.
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Parashar, Parag
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Chen, Chun H.
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Akbar, Chandni
Using machine intelligence on device and process performance prediction is an emerging methodology in the IC industry. While semiconductor technology computer-aided design (TCAD) has been researched and developed for over 30 years, it should contribute to or be used in conjunction with machine learning algorithms in solution finding procedure. Here, we propose an adaptive weighting neural network (AWNN) model that combines the advantages of statistical the machine learning model and the physical TCAD model. Using aspect ratio dependent etching as an example, our proposed AWNN outperforms conventional artificial neural network in terms of mean square errors in the test set where 5-10 times reduction is observed. The effectiveness of the TCAD AWNN model can be especially effective in the case of sampling over a vast sample space since the under-sampling problem can be compensated by the TCAD model. The large and nearly unbounded sample space is very common in IC technology, where cascaded and repeated process steps exist (similar to 150 process steps and similar to 20 masks for 90-nm CMOS process).
Intelligent Photolithography Corrections Using Dimensionality Reductions
(2019-10-01)
Parashar, Parag
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Akbar, Chandni
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Rawat, Tejender S.
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Pratik, Sparsh
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Butola, Rajat
With the shrinking of the IC technology node, optical proximity effects (OPC) and etch proximity effects (EPC) are the two major tasks in advanced photolithography patterning. Machine learning has emerged in OPC/EPC problems because conventional optical-solver-based OPC is time-consuming, and there is no physical model existing for EPC. In this work, we use dimensionality reduction (DR) algorithms to reduce the computation time of complex OPC/EPC problems while the prediction accuracy is maintained. Also, we implement a pure machine learning approach where the input masks are directly mapped to the output etched patterns. While one photolithographic mask can generate many experimental patterns at once, our pure ML-based approach can shorten the trial-and-error period in the photolithographic correction. Additionally, we demonstrate the automation in SEM images preprocessing using feature detection, and this facilitates intelligent manufacturing in semiconductor processing. The input vector dimensions are effectively reduced by two orders of magnitude while the observed mean squared error is not affected significantly. The computation runtime is reduced from 4804 s of the baseline calculation to 10 s-200 s The MSE values changed from the baseline 0.037 to 0.037 for singular value decomposition (SVD), to 0.039 for independent component analysis (ICA), and to 0.035 for factor analysis (FA).
Lithography-free thin-titanium-nanocone metamaterial perfect absorbers using ZnO nanostructures
(2017-10-01)
Lin, Albert
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Parashar, Parag
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Yang, Chih-Chieh
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Jian, Ding Rung
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Huang, Wei-Ming
In this work, thin Ti nanocones are deposited on top of the arrays of ZnO nanopagodas, and the whole structure works as an efficient nanostructured metamaterial perfect absorber (MPA) without using lithography and dry etching. In this design, similar to 1 mu m long ZnO nanopagoda arrays are grown on a 100 nm ZnO buffer layer over the silicon/glass substrate by a treatment with an aqueous solution of L-ascorbic acid. Growth direction and the degree of lamination in the ZnO nanostructures can be easily controlled by adjusting the concentration of L-ascorbic acid. Afterward, these ZnO nanopagodas are coated with a 30nm thin top and a 500nm thick bottom layer of Ti to achieve the proposed nanocone resonant cavity structure with electromagnetic wave field penetration. The overall structure encapsulates three physical concepts, namely, field penetration, adiabatic coupling and cavity resonance, which contribute the broadband perfect absorption. The entire process is carried out at a low temperature (< 90 degrees). We believe the proposed tapered Ti nanocones MPA structure facilitates ultra-broadband perfect spectral absorption with promising nature of low-cost, large-area, and lithography-free. (C) 2017 Optical Society of America
Physics-Prior Bayesian Neural Networks in Semiconductor Processing
(2019-01-01)
Chen, Chun Han
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Parashar, Parag
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Akbar, Chandni
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Fu, Sze Ming
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Syu, Ming-Ying
With the fast scaling-down and evolution of integrated circuit (IC) manufacturing technology, the fabrication process becomes highly complex, and the experimental cost of the processes is significantly elevated. Therefore, in many cases, it is very costly to obtain a sufficient amount of experimental data. To develop an efficient method to predict the results of semiconductor experiments with a small amount of known data, we use a novel method based on Bayesian framework with the prior distribution constructed by technology computer-aided-design (TCAD) physical models. This method combines the advantages of statistical models and physical models in the aspect that TCAD can provide visionary guidance on an experiment when a limited amount of experimental data is available, and a machine learning model can account for subtle anomalous effects. Specifically, we use aspect ratio dependent etching (ARDE) phenomenon as an example and use variational inference with Kullback-Leibler divergence minimization to achieve the approximation to the posterior distribution. The relation between etching process input parameters and etching depth is learned using the Bayesian neural network with TCAD priors. Using this method with 35 neurons per hidden layer, mean square error (MSE) in the test set is reduced from 0.2896 to 0.0175, 0.058 to 0.0183, 0.0563 to 0.0188, 0.058 to 0.019 for partition =10, 20, 30, 40, respectively, reference to the baseline BNN where a regular normal distribution prior with zero mean and unity standard deviation N(0,1) is used.
Polarization- selective ultra-broadband super absorber
(2017-02-20)
Zhong, Yan Kai
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Fu, Sze Ming
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Huang, Weiming
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Rung, Ding
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Huang, Jian Yi-Wen
While a broadband metamaterial perfect absorber (MPA) has been implemented and proposed intensively in recent years, an ultra-broadband perfect absorber with polarization selectivity has not been realized in literature. In this work, we propose a configuration of polarization-selective (PS) MPA with ultra-wide absorption bandwidth. The aluminum wire grid is integrated on top of the ultrathin-metal-dielectric stacking. The transverse electric (TE) wave is blocked due to the requirement of zero tangential electric field at the metal surface. The transverse magnetic field can pass the aluminum wire-grids because the normal electric field can be supported by the surface charge density at the metal surface, and full absorption of the TM wave is accomplished by the metal-dielectric stacking beneath. Theoretical calculation using rigorously coupled wave analysis demonstrates the wavelength selectivity from lambda = 1.98 mu m to lambda = 11.74 mu m where the TE absorption is <0.04 while TM absorption is >0.95, using 300 nm thick aluminum (Al) wire grid with 16-pair SiO2/Ti stacking. Additionally, the design is wavelength scalable by adjusting the dielectric thickness (t(sio2)) and the wire grid period (P) and height (t). The experimental result is demonstrated using Al grids and Ti/SiO2, and the measured result fully supports the calculated prediction. (C) 2017 Optical Society of America
The Planar Thermophotovoltaic Selective Nearly-Perfect Absorbers/Emitters
(2017-01-01)
Parashar, Parag
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Jian, Ding-Rung
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Huang, Weiming
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Huang, Yi-Wen
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Lin, Albert
The spectral response of thermophotovoltaic (TPV) absorber is a key factor for TPV system efficiency. We have shown that the planar TPV emitters can be realized by using thin-metal-dielectric stacking (IEEE Photonics J. 8, 1300109, 2016). In this paper, we provide a new structure composed of alternating dielectric and deep-sub-skin-depth (DSSD) metal layers for TPV selective absorbers. The RCWA calculation and the Fourier transform infrared (FTIR) measurement of tantalum (Ta) based planar absorber shows a high absorbance below the cut-off wavelength (lambdalambda c).
Transparent planar indium tin oxide for a thermo-photovoltaic selective emitter
(2020-09-01)
Chen, Yu-Bin
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Parashar, Parag
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Yang, Yi-Hua
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Rawat, Tejender Singh
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Chen, Shih-Wei
Designing an efficient emitter design is an important step for achieving a highly efficient TPV conversion process. Wavelength-selective emissivity, spectra match between the emitter and TPV cells, and high thermal stability are three main characteristics that must be considered before implementing the emitter. In this work, an indium tin oxide (ITO)/sapphire emitter structure is investigated for TPV application over the temperature range from 200 degrees C to 1000 degrees C. A 1-mu m-thick ITO layer is deposited on a 650-mu m-thick sapphire substrate. In addition, 50-nm-thick SiO2 is deposited on top of the ITO to enhance the performance of emitter at high temperatures. High-temperature emissivity and absorptivity measurement of the emitter samples are obtained using FTIR and a Hitachi U-4100 spectrophotometer, respectively. The resultant SiO2/ITO/sapphire/stainless-steel planar emitter structure has selective emission with high emissivity of similar to 0.8 in the 1-1.6 mu m wavelength regime at 1000 degrees C. This emission range lies at the bandgap edge of silicon TPV cells and thus can be used to harness the true potential for making a low-cost thermophotovoltaic system. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement