Papers by Author: Kazutaka Nonomura

Abstract: In the semiconductor industry, high resolution and high accuracy measurement is needed for the geometric evaluation of Si wafers. The flatness parameters are important to evaluate the wafer profile and are required to be the same level as the design rule of IC, and the tolerance for flatness is very tight. According to SEMI (Semiconductor Equipment and Materials International) standards, the required wafer flatness will be 22 nanometres by the year 2016. However, to obtain a higher resolution for sensors, the uncertainty becomes very large compared to the resolution and influences the measured data when the noise is increased. High resolution instruments always incorporate a certain degree of noise. In the presence of noise, form parameters are normally biased. Correction and compensation need a large population of measurements to analytically estimate both bias and uncertainty. The estimation is still far from perfect because of the nature of noise. Another approach is to extract a true profile by filtering noise from the measured data. For the purpose of noise reduction, low-pass filters by Gaussian smoothing and Fourier transform are often used. The noise is normally considered to be a component of small deviation (amplitude) with high frequency which also takes a normal distribution around zero. However these conventional filters can remove the noise in the spatial frequency domain only. So, it is essential to design a filter capable of removing the noise both in the spatial frequency domain and the amplitude component. Thus, we have designed and developed new type of digital filter for denoising. We introduce two new digital filters. One is wavelet transform capable of denoising in the spatial frequency domain and amplitude component, and the other is total variation that can be applied to discontinuous signals without introducing artificial Gibbs Effects.

Abstract: Recently in semiconductor industry, production of ever flatter, thinner and larger silicon wafers are required to fulfill the demands of high-density packaging and cost reduction. In geometric evaluation of Si wafers, according to SEMI (Semiconductor Equipment and Materials International) standards, the required wafer flatness approaches to the 22 nanometers by year 2016 [1]. For such application, uncertainty of measured data is encountered as a severe problem because high resolution instrument always incorporate a certain degree of noise. In order to precisely evaluate the wafer profile, it is essential to remove the noise from the measured data. Described in this paper is design and development of digital filters for denoising. Compared to the conventional low-pass filters, the developed filter by use of wavelet transform not only provides better performance of decomposition in the spatial frequency domain, but also offers the new capability of denoising in amplitude domain.

Abstract: Recently in semiconductor industry, production of ever flatter, thinner and larger silicon wafers are required to fulfill the demands of high-density packaging and cost reduction. In geometric evaluation of Si wafers, according to SEMI (Semiconductor Equipment and Materials International) standards, the required wafer flatness approaches to the 22 nanometers by year 2016 [1]. For such application, uncertainty of measured data is encountered as a severe problem because the requirement has met the limit of available instrument in terms of resolution and reliability. In order to precisely evaluate the wafer profile, it is essential to remove the noise from the measured data. Described in this paper is design and development of digital filters for denoising. In previous paper, digital filters for denoising with Haar wavelet transform are described. In this paper, the new filters by use of 2nd generation wavelet transform (lifting scheme) are proposed and show better performance of decomposition in the spatial frequency domain and amplitude domain.