• 한국컴퓨터산업학회논문지
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• 제6권2호
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• pp.295-304
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• 2005

본 논문에서는 워드지향 메모리 내에서 셀 사이의 커플링 결함을 검출하기 위한 고갈 테스트 발생(exhaustive test generation) 문제를 연구하였다. 셀 사이의 거플링 결함 모델에 따르면 n 워드를 갖는 메모리 내에서 w-비트 메모리 내용 또는 내용의 변화는 메모리 내의 s-1 워드 내용에 따라 영향을 받는다. 이때 검사 패턴 구성을 위한 최적의 상호작용 방법을 제안 하였으며, 제안한 검사 결과의 체계적인 구조는 간단한 BIST로 구현하였다.

• KIEE International Transaction on Systems and Control
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• 제2D권2호
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• pp.120-124
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• 2002

A new methodology is presented to diagnose faults in equipment plasma. This is accomplished by using neural networks as a pattern recognizer of radio frequency (rf) impedance match data. Using a match monitor system, the match data were collected. The monitor system consisted mainly of a multifunction board and a signal flow diagram coded by Visual Designer. Plasma anomaly was effectively represented by electrical match positions. Twenty sets of fault-symptom patterns were experimentally simulated with variations in process factors, which include rf source power, pressure, Ar, and $O_$2 flow rates. As an input to neural networks, two means and standard deviations of positions were used as well as a reflected power. Diagnostic accuracy was measured as a function of training factors, which include the number of hidden neurons, the magnitude of initial weights, and two gradients of neuron activation functions. The accuracy was the most sensitive to the number of hidden neurons. Interaction effects on the accuracy were also examined by performing a 2$^$4 full factorial experiment. The experiments were performed on multipole inductively coupled plasma equipment.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2015년도 제49회 하계 정기학술대회 초록집
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• pp.142.2-142.2
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• 2015

Plasma etch endpoint detection (EPD) of SiO2 and PR layer is demonstrated by plasma impedance monitoring in this work. Plasma etching process is the core process for making fine pattern devices in semiconductor fabrication, and the etching endpoint detection is one of the essential FDC (Fault Detection and Classification) for yield management and mass production. In general, Optical emission spectrocopy (OES) has been used to detect endpoint because OES can be a simple, non-invasive and real-time plasma monitoring tool. In OES, the trend of a few sensitive wavelengths is traced. However, in case of small-open area etch endpoint detection (ex. contact etch), it is at the boundary of the detection limit because of weak signal intensities of reaction reactants and products. Furthemore, the various materials covering the wafer such as photoresist (PR), dielectric materials, and metals make the analysis of OES signals complicated. In this study, full spectra of optical emission signals were collected and the data were analyzed by a data-mining approach, modified K-means cluster analysis. The K-means cluster analysis is modified suitably to analyze a thousand of wavelength variables from OES. This technique can improve the sensitivity of EPD for small area oxide layer etching processes: about 1.0 % oxide area. This technique is expected to be applied to various plasma monitoring applications including fault detections as well as EPD.

• 대한전자공학회논문지SD
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• 제39권11호
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• pp.44-52
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• 2002

반도체 기술의 발달로 인하여 메모리가 고집적화 됨에 따라 테스트의 복잡도와 시간도 같이 늘어나게 되었다. 실제로 널리 쓰이는 메모리 테스트 방법인 March 알고리듬은 DRAM에서 발생되는 고장을 검출하기 위해 고안된 것이다. 그러나 DRAM의 집적도가 증가함으로 반드시 고려해야 하는 이웃 패턴 감응 고장을 기존의 March 알고리듬으로는 테스트할 수 없고 DRAM의 이웃 패턴 감응 고장을 테스트하기 위한 기존 알고리듬들은 메모리 셀의 개수를 n이라고 할 때 $O(N^2)$의 복잡도를 갖기 때문에 테스트 시간을 많이 소요하게 된다. 본 논문에서는 메모리 테스트에 많이 쓰이는 March 알고리듬을 확장하여 메모리의 이웃 패턴 감응 고장 검출율을 효과적으로 높일 수 있는 알고리듬을 제안하였다.