Accuracy

Mean Calibration Error

3 Standard Deviations

=

±

(

)

N

MEAN

OBJ_PREDICT

OBJ_ACTUAL

1

1

E

T

T

N

=

-

å

TMP006, TMP006B

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SBOS518E – MAY 2011 – REVISED APRIL 2015

Table 10. Using the mean value minimizes error introduced by random noise. Based on the means, a set of

coefficients is generated based on a user-selected optimization criteria for Equation 7. Common criteria are

minimizing the maximum error, minimizing the average error, and so on. For a detailed discussion of optimization

methods, see user guide SBOU142, TMP007 Calibration Guide.

Table 10. Mean Values

0°C

20°C

40°C

60°C

0°C

20°C

40°C

60°C

9.2.1.2.2

Verifying the Calibration

The next step is to use the generated coefficients to verify the calibration, and determine the accuracy of the

system. For common calibration (C) , the same coefficients are used for all devices; in unit calibration (U) the

coefficients are calculated for each device. Common calibration includes device-to-device variation, and thus is

less accurate, but much easier to implement. Unit calibration is more accurate, and eliminates device variation,

but requires more effort to implement. The choice depends on the application requirements for accuracy versus

implementation effort.

Mean calibration error at each point is defined as shown in Equation 11:

where

•

•

•

N is the number of devices in the calibration set.

(11)

The mean error graph (see Figure 20) provides an efficient method of understanding how the systematic errors

vary across the temperature ranges of interest. This graph also provides a means of weighing the benefits and

efforts of common versus unit calibration for a particular application.

Note that calibration does not affect the temporal random noise observed, as shown in Figure 21. The standard

deviation of the temperature error is independent of the calibration if the random error is dominated by the sensor

noise and not external system factors, such as convection and conduction. For common calibration, the total

standard deviation increases because of the effects of device-to-device variation. This standard deviation is

The accuracy is then defined as the mean calibration error plus the random errors from all sources. For this

example application, use the criteria shown in Equation 12:

(12)

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