Apparatus and method for detecting and assessing a spatially discrete dot pattern

An apparatus for detecting and assessing a spatially discrete dot pattern disposed in a multidimensional coordinate system, in which each dot in the pattern assumes at least two differentiatable status values, the apparatus comprising:

• a measuring device for recording coordinate values and status values of each dot of a multidimensional spatial dot pattern representing a failure pattern of a physically cohesive block of memory cells of a semiconductor memory of a plurality of semiconductor memories constructed on a main surface of a semiconductor wafer;
• a memory associated with said measuring device for storing data corresponding to the recorded coordinate values and status values of each dot of the multidimensional spatial dot pattern;
• a computer associated with said memory for receiving the stored data, for determining a coordinate counter for each coordinate value of a coordinate axis from the stored data, and for forming a value of the coordinate counter from a number of detected dots of coordinates having a predetermined status value;
• and
• a neural network associated with said computer for receiving an n-dimensional input vector with components formed from the calculated coordinate counters of each dot of the spatially discrete dot pattern, for calculating an output vector by comparing the calculated input vector of the measured dot pattern with stored set-point vectors obtained on the basis of exemplary dot patterns, and for assigning a classification value of the measured dot pattern from the ascertained output vector and outputting the classification value.

A method for detecting and assessing a spatially discrete dot pattern disposed in a multidimensional coordinate system, in which each dot in the pattern assumes at least two differentiatable status values, the method which comprises:

• recording coordinate values and status values of each dot of a multidimensional spatial dot pattern with a measuring device;
• storing in a memory data corresponding to the recorded coordinate values and status values of each dot of the multidimensional spatial dot pattern;
• entering the stored data in a computer associated with the memory;
• determining a coordinate counter for each coordinate value of a coordinate axis from the stored data with the computer, and forming a value of the coordinate counter from a number of detected dots of the coordinates having a predetermined status value;
• forming n components of an n-dimensional input vector from the calculated coordinate counters of each dot of the spatially discrete dot pattern;
• assigning at least one of the following numerical values to a respective one of n components of the n-dimensional input vector:
• a proportion of coordinate values having more than a predetermined number of dots of the dot pattern having a predetermined status value;
• a proportion of echo dots of dots of the dot pattern having a predetermined status value;
• a parameter of fluctuations of dots of adjacent coordinate values of the dot pattern having a predetermined status value;
• and
• a proportion of coordinate values having fewer than a predetermined number of corresponding coordinates of dots of the dot pattern having a predetermined status value;
• entering the n-dimensional input vector into a neural network;
• calculating and outputting an output vector with the neural network by comparing the calculated input vector of the measured dot pattern with stored set-point vectors obtained on the basis of exemplary dot patterns;
• and
• assigning and outputting a classification value of the measured dot pattern from the output vector ascertained with the neural network.

A method for detecting and assessing a spatially discrete dot pattern disposed in a multidimensional coordinate system, in which each dot in the pattern assumes at least two differentiatable status values, the method which comprises:

• recording coordinate values and status values of each dot of a multidimensional spatial dot pattern with a measuring device;
• storing in a memory data corresponding to the recorded coordinate values and status values of each dot of the multidimensional spatial dot pattern;
• entering the stored data in a computer associated with the memory;
• determining a coordinate counter for each coordinate value of a coordinate axis from the stored data with the computer, and forming a value of the coordinate counter from a number of detected dots of the coordinates having a predetermined status value;
• forming n components of an n-dimensional input vector from the calculated coordinate counters of each dot of the spatially discrete dot pattern;
• assigning at least one of the following numerical values to a respective one of n components of the n-dimensional input vector:
• an equidistance of dots of the dot pattern having a predetermined status value in a periodic partial region of the dot pattern;
• and
• a number of at least partially cohesive partial regions of dots of the dot pattern having a predetermined status value in predetermined coordinate directions;
• entering the n-dimensional input vector into a neural network;
• calculating and outputting an output vector with the neural network by comparing the calculated input vector of the measured dot pattern with stored set-point vectors obtained on the basis of exemplary dot patterns;
• and
• assigning and outputting a classification value of the measured dot pattern from the output vector ascertained with the neural network.

A method for detecting and assessing a spatially discrete dot pattern disposed in a multidimensional coordinate system, in which each dot in the pattern assumes at least two differentiatable status values, the method which comprises:

• recording coordinate values and status values of each dot of a multidimensional spatial dot pattern with a measuring device;
• storing in a memory data corresponding to the recorded coordinate values and status values of each dot of the multidimensional spatial dot pattern;
• entering the stored data in a computer associated with the memory;
• determining a coordinate counter for each coordinate value of a coordinate axis from the stored data with the computer, and forming a value of the coordinate counter from a number of detected dots of the coordinates having a predetermined status value;
• forming n components of an n-dimensional input vector from the calculated coordinate counters of each dot of the spatially discrete dot pattern;
• normalizing numerical values assigned to n components of the n-dimensional input vector in a numerical range of numbers between -1 and +1;
• entering the n-dimensional input vector into a neural network;
• calculating and outputting an output vector with the neural network by comparing the calculated input vector of the measured dot pattern with stored set-point vectors obtained on the basis of exemplary dot patterns;
• and
• assigning and outputting a classification value of the measured dot pattern from the output vector ascertained with the neural network.

A method for detecting and assessing a spatially discrete dot pattern disposed in a multidimensional coordinate system, in which each dot in the pattern assumes at least two differentiatable status values, the method which comprises:

• recording coordinate values and status values of each dot of a multidimensional spatial dot pattern with a measuring device;
• storing in a memory data corresponding to the recorded coordinate values and status values of each dot of the multidimensional spatial dot pattern;
• entering the stored data in a computer associated with the memory;
• determining a coordinate counter for each coordinate value of a coordinate axis from the stored data with the computer, and forming a value of the coordinate counter from a number of detected dots of the coordinates having a predetermined status value;
• forming n components of an n-dimensional input vector from the calculated coordinate counters of each dot of the spatially discrete dot pattern;
• entering the n-dimensional input vector into a neural network;
• calculating and outputting an output vector with the neural network by comparing the calculated input vector of the measured dot pattern with stored set-point vectors obtained on the basis of exemplary dot patterns;
• and
• assigning and outputting a classification value of the measured dot pattern from the output vector ascertained with the neural network;
• and
• representing a failure pattern of a physically cohesive block of memory cells of a semiconductor memory of a plurality of semiconductor memories constructed on the main surface of a semiconductor wafer, with the dot pattern.