Determination of cutting parameters for silicon wafer with a Diamond Wire Saw using an artificial neural network

Kayabaşı, Erhan; Öztürk, Savaş; Çelik, Erdal; Kurt, Hüseyin

doi:10.1016/j.solener.2017.04.022

Cited by 41 publications

(10 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the authors carried out experiments employing kinematics similar to that of grinding and the material removal mechanism was not validated by the residual crystalline phase. Kayabasi et al (2017) investigated the effects of variations in the wire cutting speed (v c ≤ 4.5 m/s), feed rate (v f ≤ 2 mm/min) and oil/water emulsion ratio (100% and 30% oil) on the surface roughness of sawn mono-Si wafers. An artificial neural network (ANN) was accommodated to predict the surface roughness parameter R a using the data obtained from the DWS cutting experiments.…”

Section: Introductionmentioning

confidence: 99%

Effect of cutting parameters on surface integrity of monocrystalline silicon sawn with an endless diamond wire saw

et al. 2020

View full text Add to dashboard Cite

The cutting of silicon wafers using multi-diamond wire sawing is a critical stage in solar cell manufacturing due to brittleness of silicon. Improving the cutting process output requires an in-depth understanding of phenomena associated with cutting parameters. In order to investigate the influence of diamond wire sawing on surface integrity of monocrystalline silicon, a looped diamond wire was used and cutting parameters wire cutting speed, feed rate and wire tension were varied. The surface morphology was observed by scanning electron microscopy. Surface roughness S a was measured with a non-contact profilometer. The brittle-ductile transition was identified by presence of residual phases on sawn surface. A bevel-polishing method was employed to determine the microcrack depth. The results show that with higher feed rate the surface presents deeper and wider craters because of deeper penetration of diamond grain. On increasing wire cutting speed, there were more regions formed in ductile mode. The higher S a values was observed on increasing both feed rate and wire tension, while S a decreased with an increase in wire cutting speed. The brittle mode was predominant with an increase in feed rate, resulting in Si-I phase in regions formed in fragile mode. Material removal in ductile mode led to appearance of a-Si phase at high wire cutting speed. No significant effect was observed on increasing wire tension. Subsurface microcracks mainly initiating from bottom of grooves generated by cutting mechanism. The most appropriate set of cutting parameters is the lowest feed rate and wire tension and highest wire cutting speed.

show abstract

Section: Introductionmentioning

confidence: 99%

Effect of cutting parameters on surface integrity of monocrystalline silicon sawn with an endless diamond wire saw

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Literature [22] discusses the issue of sovereign debt, and they think that the debtor has the motivation to repay the debt because of the convenience of financing in the future. Literature [23] studies the motivation of enterprises to repay loans and holds that the threat of bank terminating loans will provide an incentive for enterprises to repay loans. Literature [24] applies their model to multistage dynamic environment, which also proves the existence of this mechanism of terminating loans.…”

Section: Related Workmentioning

confidence: 99%

A Self-Learning BP Neural Network Assessment Algorithm for Credit Risk of Commercial Bank

Liu

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Commercial banks play an important role in the financial system, and their role in the processes of capital circulation, capital integration, capital resource allocation, and total social demand and supply adjustment is irreplaceable. This paper proposes the idea of constructing a credit risk evaluation system for commercial banks based on BPNN by combining qualitative and quantitative analysis, based on computer technology and management theory. Its main goal is to use a neural network’s self-learning ability to perform complex loan risk assessment. BPNN is capable of self-learning, self-adaptation, and knowledge acquisition, as well as dealing well with uncertainty. It is a nonlinear method that eliminates obvious subjective and artificial factors, resulting in a more objective and effective evaluation result. Experiments show that the established model is effective in regulating personal credit management and reducing investment risks for commercial banks, as well as providing a new decision-making framework for banks’ personal credit business.

show abstract

“…In another of their articles [5], Su et al used BP neural network to predict the cutting quality of the dicing saw, and established a neural network model with blade cutting distance, blade exposure, blade wear out, and blade loading as model inputs, verified the effectiveness of the model with five groups of test data, and achieved 75% accuracy on the basis of a small amount of test data. Kayabasi et al [9] built a neural network model to predict the cut quality of a wire saw using spool speed, z-axis speed, and oil ratio in a coolant slurry and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Optimization method of cutting parameters of wafer dicing saw based on orthogonal regression design

Liu

Chen²,

Cao

et al. 2022

SN Appl. Sci.

View full text Add to dashboard Cite

Wafer dicing saw is one of the core equipment in the manufacturing process of semiconductor integrated circuit components. The cutting accuracy of dicing saw directly affects the overall quality of processed chips. This paper systematically investigates the relationship between the main cutting process parameters and the cutting quality of the dicing saw. The orthogonal experimental design method and genetic algorithm are used to optimize the cutting process parameters, solving the high cost and low efficiency problems caused by the traditional way of selecting parameters by trial and error. After optimization, the average maximum chipping width is only 38.54 μm, which is 8.23% better than the traditional way of cutting quality. Based on the blade thickness of 35 μm, the maximum chipping width reached the industry-recognized best standard of 1.1 times the blade thickness, further proving the effectiveness of the method.Article Highlights The first joint application of orthogonal regression design method and evoluti-onary algorithm for parameter optimiza-tion of dicing saw. A set of optimal cutting parameters are found and verified by experiments. Compare and contrast the optimal cutting parameters cutting’s performance.

show abstract

Determination of cutting parameters for silicon wafer with a Diamond Wire Saw using an artificial neural network

Cited by 41 publications

References 28 publications

Effect of cutting parameters on surface integrity of monocrystalline silicon sawn with an endless diamond wire saw

Effect of cutting parameters on surface integrity of monocrystalline silicon sawn with an endless diamond wire saw

A Self-Learning BP Neural Network Assessment Algorithm for Credit Risk of Commercial Bank

Optimization method of cutting parameters of wafer dicing saw based on orthogonal regression design

Contact Info

Product

Resources

About