Minimizing noise contributions of devices is important in RF circuits. This starts with the device design, and continues with the circuit design and system design. The parameters used to design or evaluate the noise performance of devices and circuits are called noise parameters. Noise parameters, used with s-parameters, provide low noise designers the information that they need.
Noise parameters typically include a set of values that describe how the noise figure of a device varies with impedance match. Note that in this document, impedance and gamma may be used interchangeably. As is known in the art, they contain equivalent information.
The noise parameters generally vary with measurement parameters such as frequency, bias, or temperature which are associated with a Device Under Test (DUT). The measurement parameters are independent stimulus values that setup specific measurement conditions. Device parameters comprise noise parameters and s-parameters, and are values that are typically measured for each desired set of measurement parameters. Gain parameters are derived from s-parameters, so may also be considered part of the device parameters.
There are different mathematical forms of the noise parameters, but generally include a set of four (4) scalar values. A commonly used set is:
1. F min=minimum noise figure.
2. Gamma_opt magnitude=magnitude of gamma_opt, the optimum source gamma that will produce F min
3. Gamma_opt phase=phase of gamma_opt, the optimum source gamma that will produce F min
4. rn=equivalent noise resistance, which determines how fast the noise figure will change as the source gamma moves away from Gamma_opt.
With this set of noise parameters, the noise figure of the device for any source gamma is then generally described by the equationF=F min+4*rn*|gamma_opt−gamma_s|{circumflex over ( )}2/(|1+gamma_opt|{circumflex over ( )}2*(1−|gamma_s|{circumflex over ( )}2))
Where gamma_s=source reflection coefficient seen by the DUT and F=Noise figure.
Other noise parameter forms include a correlation matrix (of which there are multiple configurations), and a set with forward and reverse noise parameters used by the National Institute for Standards and Technology (NIST). Generally, all of the noise parameter forms contain the same basic information. So if one form of the noise parameters is known, the noise parameters can be converted to any other form with a math formula.
Noise parameters are commonly determined by measuring the DUT under multiple impedance conditions, in a setup similar to that shown in FIG. 1. The bias system is used to apply the desired DC voltages and currents to the DUT. Then, the input and output switches are set to connect the DUT to the network analyzer, and the s-parameters of the DUT are measured with the impedance tuner set to a Z0 or matched condition. Next, the input and output switches are set to connect the DUT to the noise source and the noise receiver. The impedance tuner is then sequentially set to multiple source impedances and the raw noise data is measured with the noise receiver for each impedance setting. The raw noise data is data that is read directly from the noise receiver and other equipment that may also be used in the setup. For example, bias voltages and currents may be read from the power supplies which provide the DUT bias, or they may be read with separate voltmeters or current meters.
An alternate setup for measurement of noise parameters is shown in FIG. 2. Instead of using a noise source, a power meter is used to calibrate the noise receiver inside the network analyzer.
Another alternate setup for measurement of noise parameters is shown in FIG. 3. Here, the RF source in the network analyzer is used to create a signal, and the receivers inside the network analyzer are setup to measure the signal to noise ratios of the DUT. The noise figure of the DUT is the signal to noise ratio on the input divided by the signal to noise ratio on the output. In a practical device the output signal to noise ratio will always be smaller than the input signal to noise ratio because of the noise added by the device.
The raw noise data may be collected using the standard method, which is to measure the raw noise data at every impedance at one measurement parameter value, such as frequency. When the data collection is finished for that measurement parameter value, the process is then repeated for other measurement parameter values.
The raw noise data may also be collected using the newer fast method, which is to set the source impedance tuner to one state, and measure the raw noise data for a sweep of a measurement parameter value, for example at multiple values of frequency. The impedance tuner is then set to another state, and the raw data is measured for another sweep of the measurement parameter value. This is repeated until the raw data has been collected for every desired source impedance. With this fast method, the measurement parameter sweep could also include different values of multiple measurement parameters, such as frequency, temperature, or bias values. See, U.S. Pat. No. 8,892,380, the entire contents of which are incorporated herein by this reference.
The bias values that may be used as swept measurement parameters depend on the type of device. For example, a device such as an FET will typically be biased with two voltages, one on the output terminal of the device, and one on the input terminal of the device. Either of these voltages may be used as a swept measurement parameter during the noise parameter measurements. The input or output current may also be used as a measurement parameter in some cases. Other devices may have additional control terminals, so additional voltage or currents may be used as measurement parameters in that case. The DC bias is typically provided by using power supplies connected to the DUT using bias tees.
After collecting the data for all the desired impedance settings, the noise parameters are determined by fitting the data to the noise equations. Since the noise parameters comprise four scalar values, a minimum of four measurements are required to determine the four values. However, the noise measurement is very sensitive and measurement equipment is never perfect, so normally some small errors are included in the data. To minimize the effect of these errors, the measurement is commonly done at more than four impedance settings. This results in over-determined data which can be reduced using Least Means Squares (LMS) methods which reduce sensitivity to some of the errors. But in any practical measurement setup, there are always some residual errors. If the measurement is done at multiple frequencies, for example, the error at one frequency will be different than the error at the next frequency. In fact, the error at adjacent frequencies could move in the opposite direction, so a plot vs. frequency will show some scatter, as shown in the plots of F min and gamma_opt vs. (i.e. as a function of) frequency in FIGS. 4 and 5. The same thing can happen vs. other measurement parameters, such as a measurement vs. DC bias or temperature, for example.
This is a significant limitation of the prior art, that the noise parameter solution is determined independently for every measurement parameter value, such as frequency or bias for example. Because noise measurements are very sensitive, the noise parameters thus determined can show significant scatter vs. a measurement parameter such as frequency. However, this scatter of data comes from the measurement process, not from the device, so it is not a true representation of the device.
In the prior art, it is common to apply smoothing to plotted data after the noise parameter determination is complete, as shown in FIG. 6. This is done because the general knowledge of the device operation indicates that the true data should be smooth. But FIG. 6 still shows that the real measured values of F min have scatter. This method of smoothing data tries to account for the known fact that F min should be smooth with frequency, but is still limited by the scatter and the bandwidth of the measurement. Often the scatter is not symmetrical, and then smoothing of the scattered data will give the wrong slope to the plot. Also, measurements over a narrow band will give a slope that is very sensitive to error.