Yu-Sin Yang

Fast, exact, and non-destructive diagnoses of contact failures in nano-scale semiconductor device using conductive AFM

We fabricated a novel in-line conductive atomic force microscopy (C-AFM), which can analyze the resistive failures and examine process variance with an exact-positioning capability across the whole wafer scale in in-line DRAM fabrication process. Using this in-line C-AFM, we introduced a new, non-destructive diagnosis for resistive failure in mobile DRAM structures. Specially, we focused on the self-aligned contact (SAC) process, because the failure of the SAC process is one of the dominant factors that induces the degradation of yield performance, and is a physically invisible defect. We successfully suggested the accurate pass mark for resistive-failure screening in the fabrication of SAC structures and established that the cause of SAC failures is the bottom silicon oxide layer. Through the accurate pass mark for the SAC process configured by the in-line C-AFM analyses, we secured a good potential method for preventing the yield loss caused by failures in DRAM fabrication.

Spectroscopic vector analysis for fast pattern quality monitoring

In semiconductor industry, fast and effective measurement of pattern variation has been key challenge for assuring massproduct quality. Pattern measurement techniques such as conventional CD-SEMs or Optical CDs have been extensively used, but these techniques are increasingly limited in terms of measurement throughput and time spent in modeling. In this paper we propose time effective pattern monitoring method through the direct spectrum-based approach. In this technique, a wavelength band sensitive to a specific pattern change is selected from spectroscopic ellipsometry signal scattered by pattern to be measured, and the amplitude and phase variation in the wavelength band are analyzed as a measurement index of the pattern change. This pattern change measurement technique is applied to several process steps and verified its applicability. Due to its fast and simple analysis, the methods can be adapted to the massive process variation monitoring maximizing measurement throughput.

A new method for wafer quality monitoring using semiconductor process big data

In this paper we proposed a new semiconductor quality monitoring methodology – Process Sensor Log Analysis (PSLA) – using process sensor data for the detection of wafer defectivity and quality monitoring. We developed exclusive key parameter selection algorithm and user friendly system which is able to handle large amount of big data very effectively. Several production wafers were selected and analyzed based on the risk analysis of process driven defects, for example alignment quality of process layers. Thickness of spin-coated material can be measured using PSLA without conventional metrology process. In addition, chip yield impact was verified by matching key parameter changes with electrical die sort (EDS) fail maps at the end of the production step. From this work, we were able to determine that process robustness and product yields could be improved by monitoring the key factors in the process big data.

The Development of the Non-contact Electrical Leakage Property Measurement System for the High-K Dielectric Materials on DRAM Capacitors

We have used the non-contact electrical property measurement system to characterize the electrical leakage property of high-K materials such as Al2O3 and HfO2 on a patterned wafer. The basic technology is to measure the surface voltage with micro Kelvin probe after the corona charge deposition on a measurement area. Because of the charge decay through a dielectric material, voltage-time spectra follow exponential time dependence that is the characteristic of leakage induced charge decay. We have measured the electrical leakage property of the storage capacitors on the direct cell area of DRAM device. The measured electrical leakage property can be classified according to the thickness of Al2O3 and HfO2. Since the electrical leakage property depends on a thickness of a dielectric material, voltage-time spectra show different shapes according to the HfO2 thickness. Using the technology, we can monitor the electrical leakage property of the storage capacitors of high-K materials on the direct cell area