A.

Setup

The measurement setup includes a quartz halogen light source coupled to a filter wheel and an integration sphere, a parametric tester and interfacing mechanical and electrical adapters. The filter wheel has 8 positions, 7 incorporate narrow band filters (approximately 30 nm full width half maximum) and one is clear. The central transmission wavelengths of the filters are 438, 488, 579, 681, 780, 880 and 935 nanometres. The integration sphere output intensity is calibrated for each filter including the open slot. The calibrated integration sphere ensures uniform and controlled light intensity. The mechanical adapters position the sensor in front of the integration sphere orifice. The distance between the sensor and the integration sphere output port defines the optical F-number. In most of the measurements the sensor was positioned at a distance equivalent to F/1. The tested CMOS sensor unit requires supply of power, clock and a few control signals. All of these are supplied by the parametric tester. The same tester acquires the output of the sensor including the digital image data and some control signals. The output of the sensor is converted to a binary file and analysed using MATLAB.

B.

Full Well Capacity

The Full Well Capacity (FWC) defines the saturation level of the sensor in terms of electrical charge which in an ideal sensor is generated only by the photons signal. In general the saturation of a digital CMOS sensor may occur due to one or more limiting effects. In the CMOS TDI sensor reported here the FWC is limited by the ADC span. The ADC span is controlled electronically. Therefore it is possible to tune the FWC to different values. Fig. 3 shows the distribution of several sensors calibrated to a FWC of 280Ke-. One of the sensors was tuned to a FWC of between 250Ke- and 350Ke-. However, the design enables a wider span of calibration, estimated to be between 100Ke- and 400Ke-.

Fig. 3.

Distribution of full well capacity among population of sensors

C.

Linearity

The deviation from linearity of the sensor is defined as the peak to peak deviation from a best fit linear regression of the signal vs. the Total Illumination Energy (TIE). Changing the sensor’s electronic shutter duty cycle or exposure ratio (defined as: shutter duty cycle×1,024) is equivalent to a change in the TIE and is sometimes presented as such. Various effects degrade the linearity of the sensor at either end of the signal span. Hence, it is conventional to define the linearity within a range covering most but not all of the signal span. We define the linearity in two ranges, 5-85% and 2-95% out of the signal span. As described above each band has approx. 170,000 ROC’s. The signal vs. TIE is measured for each ROC and the peak to peak error is extracted from a linear fit of each ROC’s response. Excluding a minute number of null functioning ROC’s the worst peak to peak error is defined as the result for each band. The results shown in Table 2 are the average and standard deviation of linearity of a population of bands in the two ranges defined above.

Table 2:

Average linearity

The graph in Fig. 4 shows the linear response of the sensor demonstrating the median of the output of all ROCs at a specific signal level vs. the exposure ratio. The slope of the graph depends on the illumination intensity (TIE). In Fig. 4 a constant illumination intensity is selected to ensure that the full signal span is achieved below an exposure ratio of 500. The measured linearity is well within the requirements in Table 1.

Fig. 4.

Signal in digital levels vs. the exposure ratio (The exposure time is linearly dependent on the exposure ratio and 1024 corresponds to the maximum exposure time at a specific line rate). The luminance is 0.1 Watt/centimeter²/steradian with 488nm narrow band filter.

D.

Readout Noise

The readout noise of the sensor was measured by two methods. One method used a Photon Transfer Curve and the readout noise was extracted as the intercept of the Photon Transfer Curve with the squared noise axis. Table 3 shows the average measured readout noise of a population of sensors and the standard deviation using this method of measurement. The readout noise is expressed in terms of charge (e-) or Digital Level (DL). The distribution of the measurements in terms of charge (e-) is shown in Fig. 5.

Table 3.

Measured readout noise over population of sensors.

Fig. 5.

Distribution of readout noise among the sensor population

In the second method the pixels were turned off and the signal out of the ROCs was measured. The results are shown in Fig. 6 mapping all the ROCs of a single band and showing the spatial distribution within the band. The results in both methods are identical up to the accuracy of the measurement.

Fig. 6.

Spatial distribution of readout noise in a single band showing all the ROC (color map is in DL).

E.

Power Consumption

The sensor power consumption is dependent on several parameters (e.g. line rate). The measurement setup defines the load on the video outputs of the sensor and thus the output drives power consumption. The current setup load is significantly higher than the 20 pF specified load on each video output signal. As a result, a measurement that includes the output signal drivers is biased. To avoid bias by the over load of the output signals we measured the power consumption of the sensor without the output drivers, as shown in Fig. 7. To estimate the additional power consumption by the output drivers, one can use the following equation:

where L_rate is the line rate in units of lines/sec and B is the number of bits (12 bits max.) switched from pixel to pixel. At a line rate of 7,500 lines per second and switching all 12 bits the power consumption of the output drivers is 234mW. The calculated video output driver power consumption should be added to the measured power. The total power consumption including the output drivers is well below the requirement defined in Table 1.

Fig. 7.

Power consumption of 3 sensors (RE0062, RE0013, RE0035) at 7,500 lines per second - total of 4 bands per sensor, not including the video outpout. Measured at 3 sensor temperatures.

F.

Quantum Efficiency

Two types of sensor were manufactured: a thin absorbing layer sensor which is more suitable for the blue spectrum and a thick absorbing layer sensor which is more suitable for the red and near infrared spectrum. Both types of sensor are BSI. A simple model of absorption in silicon is suggested for both types of sensor. The model assumes perpendicular light incidence and includes the transmission of the Anti-Reflective Coating (ARC), the absorption of the silicon layer, partial reflection from the electronic circuit underneath the silicon, second absorption by the silicon layer and exit of the light. This model does not include surface effects of the silicon which are dominant in the UV and blue spectrum due to the very short absorption length. Hence, the model is inaccurate below 600 nm. The expected external QE based on the model described above and the results of measurements of both types of sensor are shown in Fig. 8. There is good agreement between the model and the measurements above 600 nm. The measurement results do not take into account a filter or a window coating. A practical device would include a window with or without a filter and the transmission of this element should also be taken into account. The sensor BSI structure absorbs light that is incident of any area above the absorbing layer. Hence, the fill factor is considered 100%.

Fig. 8.

Measured Quantum Efficiency of the thin and thick absorption layer sensors (circles and diamonds points respectively) and simulation for thin and thick absorption layers (dashed lines).

G.

MTF

The static MTF measurement setup includes a Knife Edge Target and several Resolution Targets. The Knife Edge measurement technique is less susceptible to DC errors and therefore the results are more accurate. The measurement results of the Knife Edge technique for both the thin absorption layer and the thick absorption layer sensors are shown in Fig. 9. The Resolution Target measurements were slightly higher but assumed to be less accurate and therefore are not shown here. The MTF of the thin layer sensor is expected to be higher at the blue end of the spectrum and lower at the NIR end, compared to the thick layer sensor. The measurement results are in good agreement with the expected performance.

Fig. 9.

Spectral Modulation Transfer Function measured with the thick absorption layer sensor (plus points) and the thin absorption layer sensor (circle points) at Nyquist (green) and half Nyquist (blue) frequencies.

H.

Dark Current

The dark current is linearly dependent on the exposure time. Fig. 10 shows the linear dependence of the signal measured without illumination in a dark chamber. The dark current is extracted from the slope. In order to observe the dark current, long exposure times are required, which are only achievable at very low line rates. For the thick absorption layer sensor the dark current measured at room temperature is approximately 70,000 electrons/sec/pixel (counting all 64 TDI levels) thus contributes a signal equivalent to less than 9 DL at 100 lines per second and full exposure time. In terms of noise the contribution of the dark current is equivalent to approximately 26 electrons, a negligible number compared to the readout noise. For the thin absorption layer sensor the dark current measured at room temperature is approximately 11,000 electrons/sec/pixel (counting all 64 TDI levels) which contributes a signal equivalent approximately 1 DL at 100 lines per second and full exposure time. The dark current is therefore insignificant.

Fig. 10.

Dark signal (in e-) vs. integration time of thick absorption layer sensors (blue cilrcles), and of thin absorption layer sensor (green plus), measured at 100Hz line rate allowing maximum integration time of 0.01 seconds. The dotted lines represent linear fits to the data (with zero offset).