Search

Chemotherapy-induced peripheral neuropathy (CIPN) is a major drawback in the use of chemotherapeutic agents for patients with cancer. Although studies have investigated a broad number of molecules that might be related to CIPN, the differences in the chemokine pathways between various chemotherapeutic agents, such as vincristine and oxaliplatin, which are some of the most widely used treatments, have not been fully elucidated. We confirmed that the administration (intraperitoneal injections for seven days) of vincristine (0.1 mg/kg) and oxaliplatin (3 mg/kg) induced pain by using the von Frey behavioral test. Subsequent applications with vincristine and oxaliplatin led to mechanical allodynia that lasted more than one week from the fifth day. After the induction of mechanical allodynia, the mRNA expression of CXCR2, CXCL1, CXCL3, and CXCL5 was examined in the dorsal root ganglia (DRG) and spinal cord of the CIPN models. As a result, the mRNA expression of CXCR2 robustly increased in the lumbar spinal cord in the oxaliplatin-treated mice. Next, to evaluate the involvement of CXCR2 in CIPN, reparixin, a CXCR1/2 inhibitor, was administered intrathecally or intraperitoneally with vincristine or oxaliplatin and was further verified by treatment with ruxolitinib, which inhibits Janus kinase 2 downstream of the CXCR1/2 pathway. Reparixin and ruxolitinib blocked oxaliplatin-induced allodynia but not vincristine-induced allodynia, which suggests that CXCR2-related pathways are associated with the development of oxaliplatin-induced neuropathy. Together with the above results, this suggests that the prevention of oxaliplatin-induced neuropathy by CXCR2 inhibition can lead to successful chemotherapy, and it is important to provide appropriate countermeasures against CIPN development for each specific chemotherapeutic agent.

Vibrational properties of two fullerene derivatives: C60TZ-OT-5 (1) and C60TH-3HX (2) have been studied using infrared absorption and Raman scattering spectroscopies. Additionally, quantum chemical calculations of the equilibrium geometry and normal mode vibrations of these functionalized fullerenes were performed. It was stated that despite of distinct structural differences between the investigated molecules, their experimental spectra are quite similar and correspond well with the calculated ones. The orientation of the molecules in the Langmuir–Blodgett films was evaluated.

In plasma etch process, the optical emission spectroscope (OES) is widely used to detect plasma etch endpoint. The OES gathers the intensity of the wavelength from the radicals in the plasma chamber. In addition, the OES is very sensitive to the external elements or a particles, which means that there are diverse noise in OES data. Therefore, it is necessary to choose meaningful data, reduce noise, and reduce the quantity of data. In this paper, a new method to detect endpoint of double layer plasma etching is proposed. This algorithm uses data from OES and utilizing principle component analysis (PCA) and local outlier factor (LOF).

As semiconductor devices are highly integrated, etching control is much more difficult in semiconductor industry. Plasma etching has better performance than wet etching especially in small open area etching. In semiconductor manufacturing industry, the optical emission spectroscopy (OES) is widely used to collect data from plasma chamber. The enormous OES data need to be transformed for accutate endpoint detection. There are some research results regarding endpoint detection in plasma etching using various methods such as Principal Component Analysis (PCA), Partial Least Squares (PLS), etc. In this paper a new approach is proposed for endpoint detection of small open area wafers in plasma etching. The new approach utilizes a Fast Fourier Transform (FFT), which is an efficient algorithm to detect frequency changes in signal, and this property is applied to OES data to detect endpoint in etching process. By applying FFT, noise is reduced compared to PCA data. The data after FFT is used to detect endpoint using Support Vector Machine (SVM). The combination of FFT and SVM performs excellent in terms of endpoint detection, and the performance of proposed FFT/SVM algorithm is compared to the performance of PCA/SVM algorithm.

In current semiconductor manufacturing, as the feature size of integrated circuit (IC) devices continuously shrinks, detecting endpoint in low open area plasma etch process is more difficult than before. For endpoint detection, various kinds of sensors are installed in many semiconductor manufacturing equipment, and sensor data sampled with predefined sampling rate. To solve this problem, a combination of Signal to Noise Ratio (SNR), Principal Component Analysis (PCA) and Expanded Hidden Markov model (eHMM) technique is applied to optical emission spectroscopy (OES) signals.

Since semiconductor devices are extremely integrated, process control is much more difficult in semiconductor fabrication. The optical emission spectroscopy (OES) acquires unique wavelength intensity of particles in plasma chamber, and the endpoint can be decided by utilizing its intensity. However, the endpoint detection is difficult because not only of these tremendous amount of data, but also of extremely large amount of noise in OES data. To solve these problems, the OES data of by-products and the data of etchants are classified by SNR. And a combination of Principal Component Analysis (PCA) and Support Vector Machine (SVM) hybrid algorithm is applied to detect endpoint. The PCA was utilized to reduce dimension of the selected data, and SVM algorithm is applied to separates status between before endpoint and after endpoint. The SVM model using SNR and PCA showed excellent performance in real-time endpoint detection in plasma etching.

In current semiconductor manufacturing process, plasma processes such as etch and CVD take the portion at least 40% throughout the integration processes. In etch and CVD using plasma, particles created during processes produce abnormal discharge with relatively large energy and light. This energy must be detected because this energy causes damage directly or indirectly to wafer. Using Optical Emission Spectroscopy (OES), it is possible to detect status changes of plasma and also to find arc generation. The OES is often used for analysis of plasma physics and chemistry in real-time. The OES system gathers a series of optical emission intensity of the plasma in the chamber. But OES has large amount data because of using the full range of wavelength. Therefore, it takes a long time to process the data. To solve this problem, a new method is proposed using Fuzzy model and Dempster-Shafer theory by utilizing equipment data from matching network. Six data from matching network such as Cap position, RF reflective powers, are used to make Fuzzy model for arc detection. Fuzzy model normalizes these matching network data between 0 and 1. Output of Fuzzy model is used as a reference to detect abnormal data from on-line data. Six matching network parameter are compared at the same time and Dempster-Shafer theory is used to make a decision whether current state is in arc state or not. The results show that the proposed method detects arc point accurately.

Bosch process is developed for advanced microstructure devices and etch endpoint detection (EPD) is demanded for 'notching' (as feature profile degradation) or reducing thickness of the underlying stop. One method commonly used to detect plasma process endpoint is utilizing optical emission spectrometry (OES) sensor. OES analyzes the light emitted from plasma source to draw inferences about the chemical and physical state of the plasma process. In this paper, an endpoint detection algorithm conjunction with Principal Component Analysis (PCA) and extended Hidden Markov Model (eHMM) using OES signal in Bosch process is proposed. PCA is used to reduce dimension of data without information loss and eHMM is applied to correctly detect endpoint. In 120 um TSVs, this work shows excellent performance.

In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model. The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.

In the semiconductor manufacturing process, the endpoint of plasma etch process can be determined by the graphics based detection in order to avoid the loss of over-etching and under-etching. Our approach in current study can be conducted as one way to real-time monitor and judge the endpoint instead of observing it manually. When the endpoint occurs, this system can improve the etch processes and provide instant shutdown recommendations. This method makes use of Radial Basis Function (RBF) network’s functional approximation in time-series modeling and in pattern classification. By training with enough samples, the judge will be more accurate. All the samples are probed with optical emission spectroscopy (OES) sensor in real plasma etch process and for both network training and test.