Inotera Memories, Inc.

Yij Chieh Chu

Yun-Zong Tian

Chun Chi Chen

Cheng-Hao Chen

Wei Jun Chen

Shih Chang Kao

Yi Feng Lee

Shih-Chang Kao

A fault detection method of semiconductor manufacturing processes is disclosed. The method includes the steps of providing a storage device, collecting a fault detection and classification(FDC) parameter by the storage device, setting up a measurement site for measuring an online measurement parameter, collecting a wafer acceptance test(WAT) in correspondence to the FDC parameter, establishing a first relationship equation between the FDC parameter and the online measurement parameter, establishing a second relationship equation of the online measurement parameter and the WAT by using the first relationship equation, establishing a third relationship equation between the FDC parameter and the WAT, establishing a waning region of the manufacturing processes by using the first, second, and third relationship equations, and determining the situation of generating wafer defects according to the warning region. The present invention discloses a system architecture for the method.

Fault detection method of semiconductor manufacturing processes and system architecture thereof

A method of detecting variance by regression model has the following steps. Step 1 is preparing the FDC data and WAT data for analysis. Step 2 is figuring out what latent variable effect of WAT data by Factor Analysis Step 3 is utilizing Principal Component Analysis to reduce the number of FDC variables to a few independent principal components. Step 4 is demonstrating how the tools and FDC data affect WAT data by Analysis of covariance model, and constructing interrelationship among FDC, WAT and tools. The interrelationship can point out which parameter effect WAT significantly. By the method, when WAT abnormal situation happened, it is easier for engineers to trace where the problem is.

Method for detecting variance in semiconductor processes

The instant disclosure relates to a raw data compression method for the fabrication process. The method includes the steps of: inputting into a signal converter a collection of raw data points representing operational parameter of a semiconductor equipment within a predetermined time period; obtaining an approximation of the raw data points with a Fourier series; computing the Fourier coefficients and the residuals between the raw data points and the corresponding predicted values predicted by the Fourier series; determining if the residuals exceed an error threshold; recording and storing the Fourier coefficients as the compressed data if none of the residuals exceeds the error threshold; and recording the raw data point as abnormal data point if the corresponding residual exceeds the error threshold before recording and storing the Fourier coefficients and the abnormal data point as the compressed data.

Method and system of compressing raw fabrication data for fault determination

A specification establishing method for controlling semiconductor process, the steps includes: sampling a plurality of sample groups from a population, each sample group being a non-normal distribution; filtering the sample groups; summarizing the filtered sample groups to form a non-normal distribution diagram; getting a value-at-risk and a median by calculating from the non-normal distribution diagram; getting a critical value by calculating the value-at-risk and the median with a critical formula; getting a plurality of state values by calculating the filtered sample groups with a proportion formula; and getting an index value by calculating the non-normal distribution diagram with the proportion formula. Thus, the state values indicate the states of the sample groups are abnormal or not by comparing the state values to the index value.

Specification establishing method for controlling semiconductor process

A method for assessing data worth for analyzing yield rate includes: getting measured data with data points that corresponds to control variables of semiconductor manufacturing; transforming the data points into a distance matrix with matrix distances corresponding to differences of the data points under the control variables; expressing sample differences recorded in the distance matrix by two-dimension vectors and calculating similarity degrees of the two-dimension vectors and the distance matrix so as to take loss information as a conversion error value; calculating discriminant ability of the transformed two-dimension data and expressing the discriminant ability by an error rate of discriminant; and taking the conversion error value and the error rate of discriminant as penalty terms and calculating a quality score corresponding to the measured data. Thereby, before analyzing the yield rate of semiconductor manufacturing, analysts can determine whether data includes information affecting the yield rate based on the quality score.

Method for assessing data worth for analyzing yield rate

A method of adjusting wafer process sequence includes steps of collecting production parameters for a plurality of lots; selecting a plurality of key parameters from the production parameters, wherein the key parameters at least includes a processing sequence; defining a formula to obtain an epsilon value; categorizing the lots into groups according to the epsilon value and the minimum point number by using density-based spatial clustering of application with noise (DBSCAN); and adjusting the processing sequences of the lots in the groups. Thereby, the lots with the same process recipe can be continuously or simultaneously sent into a machine, thereby reducing replacement of process recipes or shortening machine idle time.

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Taipei, Taiwan

Yij Chieh Chu