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SEMI T10-0701 © SEMI 2001 2 NOTE 1: Error correction techni ques are describ ed exte nsive ly in AIMI Unif orm Sym bology Specif ication - Data Matrix. 5.1.11 f inder pattern, of a Data Matr i x symbol a perimeter to t…
SEMI T10-0701 © SEMI 20011
SEMI T10-0701
TEST METHOD FOR THE ASSESSMENT OF 2D DATA MATRIX DIRECT
MARK QUALITY
This specification was technically approved by the Global Traceability Committee and is the direct
responsibility of the North American Traceability Committee. Current edition approved by the North
American Traceability Committee on March 20, 2001. Initially available on SEMI at www.semi.org May
2001; to be published July 2001.
1 Purpose
1.1 This standard is intended to define the
methodology for the assessment of 2D Data Matrix
direct mark quality.
2 Scope
2.1 This standard defines assessme nt criteria,
metrology methods, and assessment reporting
procedures as applied to 2D Data Matrix code direct
marks on semiconductor related materials. Application
specifications, which refer to this standard, may further
limit its scope to more specific requirements of a
particular application.
2.2 This standard does not purport to address safety
issues, if any, associated with its use. It is the
responsibility of the user of this standard to establish
appropriate safety and health practices and determine
the applicability of regulatory limitations prior to use.
3 Limitations
3.1 This standard will not specifically address the
quality assessment of paper-printed labels and will be
limited to the usage of the Data Matrix symbology as
defined by the AIMI standard.
3.2 This standard will not specify the resulting
minimum, maximum or nominal values of any direct
mark assessment method which would be considered
acceptable for a 2D Data Matrix direct mark
application. Application specifications may define
these values with reference to this standard.
4 Referenced Standards
4.1 AIM International Technical S pecification
1
AIMI Uniform Symbology Specification - Data Matrix
5 Terminology
5.1 Definitions of terms relating to 2D Data Matrix
direct mark assessment are as follows:
1 AIM International, Inc., 11860 Sunrise Valley Drive, Suite 100,
Reston, VA 20191, tel 703.391.7621, fax 703.391.7624
5.1.1 alternating pattern of a Data Matrix code
symbol a line of alternately filled and unfilled cells
indicative of the cell spacing along one of the major
axes of the Data Matrix symbol (see Figure 1).
5.1.2 binary value a mark in the substrate surface
indicates the binary value of one. The absence of a
mark, or a smooth surface surrounding a cell center
point, indicates the binary value of zero.
5.1.3 cell, of a Data Matrix symbol the area within
which a marking may be placed to indicate a binary
value. The cell is the smallest element of a two-
dimensional Data Matrix symbol. The cell shape is
generally quadrilateral, typically rectangular and ideally
square.
5.1.4 cell center point, of a Data Matrix symbol the
point at which the centerline of a matrix row intersects
the centerline of a column.
5.1.5 cell size, of a Data Matrix sym bol the number
of image pixels within a Data Matrix symbol cell.
Since the cell is generally rectangular in shape, the
resolution is specified in both the horizontal and
vertical directions of the cell.
5.1.6 cell spacing, of a Data Matrix symbol the
vertical or horizontal distance between the cell center
points of contiguous cells.
5.1.7 centerline, of a row or a column the line
positioned parallel to, and spaced equally between, the
boundary lines of the row or column.
5.1.8 edge the location of a signi ficant change in
pixel brightness values between regions. It is the
point(s) that has the greatest amount of contrast
difference (change in intensity values) between pixels.
5.1.9 edge detection method a me thod whereby the
location of an edge in an image is determined.
5.1.10 error correction mathemati cal techniques,
which reconstruct the original information, based upon
the remaining data in a damaged or poorly marked
code. Reed Solomon and convolution are two such
techniques.
SEMI T10-0701 © SEMI 2001 2
NOTE 1: Error correction techniques are described
extensively in AIMI Uniform Symbology Specification - Data
Matrix.
5.1.11 finder pattern, of a Data Matrix symbol a
perimeter to the data region. Two adjacent sides
contain marks in every cell: these are used primarily to
define physical size, orientation and symbol distortion.
This is often referred to as the L finder pattern. The
two opposite sides are made up of cells containing
marks in alternate cells (see Figure 1).
5.1.12 grayscale value
the assignment of a digital
value to a degree of light intensity. The shades of gray
are used by a computer to reconstruct an image. A
common scale is 256 shades of gray, with 0 being black
and 255 being white.
5.1.13 histogram a graphic representation of a
frequency distribution of pixel values within an area of
interest in a two-dimensional grayscale digital image.
The horizontal axis of the graph represents the range of
possible grayscale values in the image. The vertical
axis of the graph represents the frequency of occurrence
of each grayscale value in the area of interest (see
Figure 2).
5.1.14 image coordinates
location s in a two-
dimensional digital image are referenced by a two-
dimensional orthogonal coordinate system. The datum
for the coordinate system is in the upper-left corner of
the image. The horizontal axis or x-axis is located along
the top of the image, with increasing positive values
from left to right in the image. The vertical axis or y-
axis is located along the left side of the image, with
increasing positive values from top to bottom in the
image.
5.1.15 mark a cell or area of a Data Matrix symbol,
which has been marked, meaning the substrate has been
altered by the marking process so as to significantly
alter its contrast when imaged. Also can refer to an
entire Data Matrix symbol that has been applied in rows
and columns on a substrate by a marking process.
5.1.16 pixels picture elements. In a two-
dimensional digital image, pixels are individual
elements to which a grayscale value is associated. The
combination of grayscale values for each pixel and its
respective location in a two-dimensional plane form a
digital representation of a real scene.
5.1.17 pixel resolution
the precision in pixels at
which all measurements are performed. The maximum
pixel resolution shall be 1 pixel.
Figure 1
Data Matrix Symbol
SEMI T10-0701 © SEMI 20013
# of Pixels
Grayscale Value
Figure 2
Pixel Histogram
5.1.18 quiet zone areas of space surrounding the
machine-readable symbol. Quiet zone requirements
may be found in application and symbology
specifications. Sometimes called “Clear Area” or
“Margin.”
5.1.19 space
an unmarked cell or area of a Data
Matrix Symbol.
5.1.20 symbol a machine-readable pattern
comprised of a quiet zone, finder pattern, symbology
characters (which include special functions and error
detection and/ or correction characters) required by a
particular symbology.
5.1.21 symbol contrast the difference in grayscale
values between the marked and unmarked areas of a
Data Matrix symbol.
6 Summary of Method and R equirements
6.1 Test Image
6.1.1 A test image of the mark shall be obtained in a
configuration that mimics the typical reading
configuration for that mark, at substantially the same
resolution, illumination, optical focus, image exposure,
gain or other image signal preprocessing settings that
are used during reading. Individual applications may
dictate a specific wavelength or color temperature and
spatial construction of illumination, image resolution,
optical focus, image exposure, etc. The mark/reader
configuration shall be consistent to the extent that
repeatable results can be obtained from a single sample
over many instances of one mark/reader configuration.
6.1.2 The intent of the assessment p rocedure is to
regard a test image from the actual mark/reader
configuration as the source for all assessment.
Therefore, the assessment criteria and the algorithms
used to carry out each assessment should be resident in
the actual reading system or equivalent that is used to
read the mark. If a separate assessment or verification
system, which has different illumination characteristics,
resolution, etc., is used, uncorrelated results may occur.
6.2 Edge Detection Method
6.2.1 Many of the assessment proce dures depend
significantly on the type of edge detection method that
is employed to locate edges in the test image which
define the bounds of the entire Data Matrix symbol as
well as individual cells within the matrix. The edge
detection method used should be consistent throughout
the assessment process.