Hae-Jeong Yang

Semi-Continuous Interpolation Algorithm for Aspherical Surface Grinding

This paper proposed a tracking algorithm for the tool path of an aspherical surface in the grinding process. The algorithm is based on the concept that an operator simply puts in lens constants on the machine, and the grinding tool follows the lens surface. The ideal equation of a grinding tool path was derived from an aspherical lens equation. The path was sectioned by an arbitrary interval. The inputs of the algorithm, the acceleration and velocity, were determined from the position error at the beginning of the step. The velocity and the acceleration were updated by the position error and velocity of the previous step. This update smoothed the tool path and increased continuity. The algorithm was demonstrated on a 2-axis machine which had ball-screws and air-guide. The tool moved on both convex and concave surfaces by the proposed algorithm. Error was monitored by the change of the tool diameter. The maximum position error was under sub-micro level.

The Wafer Alignment Algorithm Regardless of Rotational Center

Wafer alignment is a sequence of translational and rotational movement, so typical alignment algorithms require a rotational center. The method proposed in this paper simplifies the alignment algorithm regardless of the placement of a rotational center. The original algorithm was derived from rigid body transformation, so it had the terms for the rotational center. These terms can be omitted if we make some assumptions on the characteristics of wafer alignment. The derived equation had a form similar to that of root finding in numerical analysis. The algorithm was experimented on the dicing machine and its results were compared with the exact solutions. The result showed that the simplified form was effective enough to be applied for manufacturing. And the iteration speed can be varied by the convergence constant. The derived equation was so simple that the alignment algorithm can be applied to PLC-based systems

Nano Control Algorithm for Grinding and Polishing Aspherical Surface

A position control method for interpolating aspherical grinding and polishing tool path was reviewed and experimented in a nano precision machine. The position-base algorithm was reformed from the time-base algorithm, proposed in the previous study. The characteristics of the algorithm were in the velocity control loop with position feedback. The aspherical surface was divided by an interval at which each velocity and acceleration were calculated. The theoretical velocity was corrected by position error during processing. In the experiment, a machine was constructed and nano-scale linear encoders were installed at each axis. Relation between process parameters and the variation of position error was monitored and discussed. The best result from optimized parameters showed that the accuracy was 150nm and improved from the previous report.

Convergence Analysis of Wafer Alignment Algorithm Based on Object Transformation

This paper analyzes the convergence of the automatic wafer alignment algorithm, which is similar to numerical root-finding. An iterative, simple equation for wafer alignment with a convergence constant was derived from object transformation. The convergence speed of the iteration could be varying the convergence constant eta. To demonstrate the algorithm, an experiment for an alignment was done on the dicing machine. The alignment was repeated until the error was under sub-pixel level on the inspection system. The constant was varied to monitor its effect on iterative alignment speed. The result showed the convergence speed could be controlled but the compensation values found by the iterative action had the same solution

A self-learning method for automatic alignment in wafer processing

We propose a self-learning method for automatic wafer alignment in the semiconductor manufacturing process. A feed forward neural network is trained by and used for wafer alignment. The network determines the movement of kinematic parts from the misalignment inspected by machine vision. However, it is time-consuming and inconvenient to obtain training data in this way. So, we built an automatic learning rule to gather the data and train the network. The network may determine wrong outputs and cause other misalignments at first, but the error can decrease as the training proceeds. The training sets consisted of a variation of misalignment data and the movement of an alignment stage. Five recent sets are used for training and others are dismissed or forgotten. This re-trained network tried aligning, measured misalignment, and made new training sets. This sequence makes it possible to acquire alignment skill and automate the process. After learning, automatic alignment accomplished sub-pixel accuracy for several cases of misalignment. The result showed that the proposed method could be applied to the semiconductor manufacturing process. Its performance improved about 6% compared with conventional algorithms.

A Precise Inspection Technique for Wafer Pre-sawing Lines using Affine Transformation

This study proposes a simple and fast technique for detecting wafer cutting lines using the Affine transformation before sawing. This technique was developed for the purpose of wafer alignment when alignment marks are unavailable. The edges of the pre-sawing lines are complex and there are bright shapes in the sawing lines, so it is difficult to apply the conventional edge finding method. The sawing lines are represented by rectangular windows which are deformed by an Affine transformation. The amount of deformation is determined when the grey level in the window is a global minimum in the image. The global minimum is found by the golden section search. The algorithm was tested using 13 sample images and repeated 10 times for each image. The results showed that the processing time was below 1200 m sec and the deviation was in the sub-pixel level. We found that the proposed method can be applied to align wafers in the dicing process.

Software Architecture and Subclassing Technique for Semiconductor Manufacturing Machines

In this paper, we proposed a software architecture for operating an automatic semiconductor manufacturing machine. Nowadays, machines used for semiconductor processing require a high level of automation in the areas of motion control, machine vision, data acquisition and networking. These functions are assured by industrial equipment that is generally installed in a computer. This equipment occupies a large part of the system resources and generates a large computational burden, so the software should be designed for efficiency. The proposed architecture consists of four layers and virtual equipments(VEs). The VEs are made by subclassing the physical equipments(PEs), and the layers are coded into a thread which updates the status of the VEs. Subroutines in the program p to the pointers to the VEs, and direct access to the physical equipment is prohibited. The number of accesses(NOAs) to the PEs in a typical industrial application was simulated for an unlimited access structure and the proposed structure. The result showed that the proposed structure was more efficient than the typical one irrespective of the subroutines. This architecture was also applied to design machine operating software for performing automatic wafer dicing.