• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.285-285
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• 2011

EUVL (Extreme Ultra Violet Lithography) is one of competitive lithographic technologies for sub-30nm fabrication of nano-scale Si devices that can possibly replace the conventional photolithography used to make today's microcircuits. Among the core EUVL technologies, mask fabrication is of considerable importance since the use of new reflective optics having a completely different configuration compared to those of conventional photolithography. Therefore new materials and new mask fabrication process are required for high performance EUVL mask fabrication. This study investigated the etching properties of SnO2 (Tin Oxide) as a new absorber material for EUVL binary mask. The EUVL mask structure used for etching is SnO2 (absorber layer) / Ru (capping / etch stop layer) / Mo-Si multilayer (reflective layer) / Si (substrate). Since the Ru etch stop layer should not be etched, infinitely high selectivity of SnO2 layer to Ru ESL is required. To obtain infinitely high etch selectivity and very low LER (line edge roughness) values, etch parameters of gas flow ratio, top electrode power, dc self - bias voltage (Vdc), and etch time were varied in inductively coupled Cl2/Ar plasmas. For certain process window, infinitely high etch selectivity of SnO2 to Ru ESL could be obtained by optimizing the process parameters. Etch characteristics were measured by on scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) analyses. Detailed mechanisms for ultra-high etch selectivity will be discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.315-319
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• 2007

A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.10 no.6
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• pp.373-377
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• 2000

For 6H-SiC single crystals which was obtained by sublimation growth (modified Lely process), the relation between the defects and the etch pits to be formed at the site of dislocations were discussed. Typical hexagonal etch pits were formed on (0001) basal plane. The similar hexagonal etch pit shapes were formed on the site of micropipe defects and it was realized that internal planar defects was formed with the same matrix crystal structure as grown crystals, through the observation of the etching morphology at those internal defects.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.9
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• pp.64-71
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• 1997

A new etching technique of meltback was investigated for GaAs lensed optical devices with selective windows opending in the LPE (liquid phase epitaxy) system. In the meltback process, the etch depth and the etch shape were controlled by the degree of under-saturation, etch time and other parameters. A GaAs/AlGaAs DH layer was grown on the selectively etched hemispherical well for optical device application such as lensed surface emitting LED. The regrowth process were related with the coolin grate and the well to well spacing. A novel surface emitting LED with hemispherical AlGaAs lens was fabricated using the meltbakc and regrowth as the key process for AlaAs lens array. The light emitting efficiency of the LED was upto three times higher than the similar structure LED without lens. The meltback and regrowth technique was applicable to manufacture the optical device in LPE.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.599-602
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• 2003

A new technique is presented to construct predictive models of plasma etch processes. This was accomplished by combining a backpropagation neural network (BPNN) and a random generator (RC). The RG played a critical role to control neuron gradients in the hidden layer, The predictive model constructed in this way is referred to as a randomized BPNN (RG-BPNN). The proposed scheme was evaluated with a set of experimental plasma etch process data. The etch process was characterized by a 2$^3$ full factorial experiment. The etch responses modeled are 4, including aluminum (Al) etch rate, profile angle, Al selectivity, and do bias. Additional test data were prepared to evaluate model appropriateness. The performance of RC-BPNN was evaluated as a function of the number of hidden neurons and the range of gradient. for given range and hidden neurons, 100 sets of random neuron gradients were generated and among them one best set was selected for evaluation. Compared to the conventional BPNN, the proposed RC-BPNN demonstrated about 50％ improvements in all comparisons. This illustrates that the RG-BPNN of multi-valued gradients is an effective way to considerably improve the predictive ability of current BPNN of single-valued gradient.

• Journal of IKEEE
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• v.18 no.4
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• pp.532-535
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• 2014

This paper analyzed thermal characteristics of super junction MOSFET using process and design parameters. Trench process is very important to super junction MOSFET process. We analyzed the difference of temperature, thermal resistance, total power consumption according to trench etch angle. As a result we obtained minimum value of temperature difference and thermal resistance at $89.3^{\circ}$ of trench etch angle. The electrical characteristics distribution of super junction MOSFET is not showed tendency according to trench etch angle. We need iterative experiments and simulation for optimal value of electrical characteristics. The super junction power MOSFET that has superior thermal characteristics will use automobile and industry.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.213-213
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• 2010

Magnetic random access memory (MRAM), based on magnetic tunnel junction (MTJ) and CMOS, is one of the best semiconductor memories because it can provide nonvolatility, fast access time, unlimited read/write endurance, low operating voltage and high storage density. For the realization of high density MRAM, the etching of MTJ stack with good properties is one of a key process. Recently, there has been great interest in the MTJ stack using MgO as barrier layer for its huge room temperature MR ratio. The use of MgO barrier layer will undoubtedly accelerate the development of MTJ stack for MRAM. In this study, high-density plasma reactive ion etching of MgO films was investigated in an inductively coupled plasma of $Cl_2$/Ar gas mixes. The etch rate, etch selectivity and etch profile of this magnetic film were examined on vary gas concentration. As the $Cl_2$ gas concentration increased, the etch rate of MgO monotonously decreased and etch slop was slanted. The effective of etch parameters including coil rf power, dc-bais voltage, and gas pressure on the etch profile of MgO thin film was explored, At high coil rf power, high dc-bais voltage, low gas pressure, the etching of MgO displayed better etch profiles. Finally, the clean and vertical etch sidewall of MgO films was achieved using $Cl_2$/Ar plasma at the optimized etch conditions.

• Journal of the Korean Vacuum Society
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• v.6 no.3
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• pp.267-274
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• 1997

Silicon shallow trenches applied to the STI (Shallow Trench Isolation) of integrated circuits were etched using inductively coupled $Cl_2$ and HBr/$Cl_2$ plasmas and the effects of process parameters on the etch profiles of silicon trenches and the physical damages on the trench sidewall and bottom were investigated. The increase of inductive power and bias voltage in $Cl_2$ and HBr/$Cl_2$ plasmas increased polysilicon etch rates in general, but reduced the etch selectivities over nitride. In case of $Cl_2$ plasma, low inductive power and high bias voltage showed an anisotropic trench etch profile, and also the addition of oxygen or nitrogen to chlorine increased the etch anisotropy. The use of pure HBr showed a positively angled etch profile and the addition of $Cl_2$ to HBr improved the etch profile more anisotropically. HRTEM study showed physical defects formed on the silicon trench surfaces etched in $Cl_2/N_2$ or HBr/ $Cl_2$ plasmas.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.1
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• pp.1-5
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• 2005

A new model of plasma etch process was constructed by using a radial basis function network (RBFN). This technique was applied to an etching of silicon carbide films in a NF$_3$ inductively coupled plasma. Experimental data to train RBFN were systematically collected by means of a 2$^4$ full factorial experiment. Appropriateness of prediction models was tested with test data consisted of 16 experiments not pertaining to the training data. Prediction performance was optimized with variations in three training factors, the number of pattern units, width of radial basis function, and initial weight distribution between the pattern and output layers. The etch responses to model were an etch rate and a surface roughness measured by atomic force microscopy. Optimized models had the root mean-squared errors of 26.1 nm/min and 0.103 nm for the etch rate and surface roughness, respectively. Compared to statistical regression models, RBFN models demonstrated an improvement of more than 20 % and 50 % for the etch rate and surface roughness, respectively. It is therefore expected that RBFN can be effectively used to construct prediction models of plasma processes.

• ETRI Journal
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• v.24 no.3
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• pp.211-220
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• 2002

This study characterizes an oxide etching process in a magnetically enhanced reactive ion etching (MERIE) reactor with a $CHF_3/CF_4$ gas chemistry. We use a statistical $2^{4-1}$ experimental design plus one center point to characterize the relationships between the process factors and etch responses. The factors that we varied in the design include RF power, pressure, and gas composition, and the modeled etch responses were the etch rate, etch selectivity to TiN, and uniformity. The developed models produced 3D response plots. Etching of $SiO_2$ mainly depends on F density and ion bombardment. $SiO_2$ etch selectivity to TiN sensitively depends on the F density in the plasma and the effects of ion bombardment. The process conditions for a high etch selectivity are a 0.3 to 0.5 $CF_4$ flow ratio and a -600 V to -650 V DC bias voltage according to the process pressure in our experiment. Etching uniformity was improved with an increase in the $CF_4$ flow ratio in the gas mixture, an increase in the source power, and a higher pressure. Our characterization of via etching in a $CHF_3/CF_4$ MERIE using neural networks was successful, economical, and effective. The results provide highly valuable information about etching mechanisms and optimum etching conditions.