Table 8. Wide-Range Parameters

Table 9. Wide-Range Measurement Values

9.2.1.2.1 Wide-Range Calibration

To begin calibration, select an object temperature (T_OBJ) and a value for the die temperature (T_DIE). With these system temperatures stable, take a statistically significant number of samples of V_Sensor (results shown in register 00h).

In this example, 64 samples were taken.

To compensate for first order drift in system temperatures, it is often useful to normalize the data set. For this purpose, for each temperature set, the sensor voltage data (given in register 00h) is normalized by first finding the best fit line of the form shown in Equation 9:

Equation 9.

The normalized data for each data set is then calculated as shown in Equation 10:

Equation 10.

The normalized data, V_{Sensor_norm}, is centered on zero mean, and is first-order corrected for long-term drift. The standard deviation for each data set is then calculated to estimate the sensor noise, σ. Verify that the data are limited by white noise and no other effects. For a sensor-noise-limited data set, v_SENSOR σ is typically < 1 μV, and preferably < 0.5 μV after first-order correction for drift, as described previously. If this condition is not satisfied, then the calibration accuracy is limited by external system factors (for example, convection or conduction). Repeat this process for each combination of T_OBJ and T_DIE for which the calibration is to be performed. The normalized data are used only for evaluating the suitability of the data set for calibration, and not for the actual calibration itself.

For calibration, the mean value, <V_SENSOR>, is calculated for each combination of T_OBJ and T_DIE, as shown in 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.

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:

Equation 11.

where

• T_{OBJ_PREDICT} is the temperature based on the calibration coefficients.
• T_{OBJ_ACTUAL} is the known object temperature, measured independently.
• N is the number of devices in the calibration set.

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 calculated in the usual way, by substituting T_{OBJ_PREDICT} for the mean in the standard deviation formula.

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:

Equation 12.