System Calibration and Measurement

As high frequency characterization was conducted on the devices (DUT), the applied signals with short wavelength are comparable to the probe, cables, adapters, bonding wires, and DUT. Thus, losses caused by the mentioned connecting elements will seriously degrade the measurement accuracy and resolution, and the impact becomes particularly critical with increasing frequency.. On the other hand, a measurement system has its own system error.

Consequently, a system calibration should be performed to take those losses into consideration. The standard procedure is to calibrate the system errors and then shift the measurement signal reference plane to the DUT plane. The validity and accuracy of the calibration results depend on the calibration method used.

The calibration procedure is conducted through the following six steps. Following the system calibration, the RF probe must be probed on short, open, load, and thru dummy pad;

in other calibration steps, the RF probe is probed on the same thru dummy pad.

1. Short, open, and load (SOL) calibration : Connected with a short, open, load in the place of the noise source on MNS to perform S22 measurement. This raw data will be combined with the data achieved through full two-port calibration to determine the s-parameters of the MNS.

2. Noise source calibration : First, connect the noise source to the MNS, which have established a reference plane from the SOL calibration. Then, make S-parameters measurement with the noise source at hot/cold to calculated the corresponding reflection coefficients and noise power for the noise source. This noise power measurement is used later to establish the gain and noise figure for the receiver.

3. S-parameters calibration : This step is a standard s-parameters calibration. There are many calibration methods like short-open-load-thru (SOLT), line-reflect-match (LRM), thru-reflect-line (TRL). In this thesis, we used the SOLT calibration method. This calibration

step was conducted in order to shift the reference plane to the DUT plane, and later for the noise figure calculation.

4. Thru-delay calibration : Once the S-parameter calibration is finished, then system will check the thru delay automatically. For our currently measurement, the thru delay approximates 1ps.

5. RRM and MNS calibration : For MNS calibration, S22 measurement was carried out by changing the impedance states of the solid state tuner to calibrate the source reflection coefficients. These impedances are then referred to the S-parameters port 1 reference plan.

For RRM calibration, S11 measurement was performed to determinate the input reflection coefficient of the receiver. This information is referred to the S-parameter port 2 reference plan.

6: System noise parameter calibration : In the last step, the noise power with varied source impedance is measured and the receiver noise parameters are stored at the port 2 reference plan.

After calibration, noise measurement reference plane is then established. Before noise measurement, we can measure the noise figure on the thru pad to verify the calibration result.

Theoretically, the thru pad didn’t contribute the noise. Therefore, the noise figure we measured must be less than 0.1dB. In the beginning of noise measurement, S-parameters measurement at the DUT reference plane should be done first. These S-parameters are necessary information for calculating noise parameters in next step. In the following, the electronic tuner (MNS) will vary the impedance to change the source reflection coefficient (Γs) around the Smith chart. Then the output noise power of DUT via the receiver as a function of Γs was measured. As a result, each individual Γs and its corresponding noise power construct a set of equations. Basically, Only four input states are needed for noise parameters characterization because the noise parameters calculation equation has only four unknown

parameters. In practice, in order to reduce the random system error, we usually measured more than four states, in our experience, we generally collect data from 24 or 32 states. Then a proper fitting procedure was performed to extract these four parameters. Finally, the four noise parameters: NFmin, Rn,Re(Γopt) or Re(Yopt) and Im (Γopt) or Im(Yopt) are obtained.

In the measurement process, the overall noise figure was calculated by Y-factor method technique. The overall noise figure is then under a noise figure correction step to determine the noise figure of the DUT. Details of Y-factor method and noise figure correction are included in Appendix A.

+ R

-v

R

n R i

(a) (b) (c) R

+

-v

Fig. 2.1 (a) Equivalent network for computing thermal noise of a resistor. (b)(c) Thermal noise model for a resistor.

Source Drain

P-substrate

Gate L W

0 x L_eff

Q_I(x)

x+dx v(x)

E, lateral field

Fig. 2.2 Schematic diagram of a MOSFET operated in saturation condition.

(a) (b)

Fig .2.3 Schematic for BSIM4 channel thermal noise modeling (a) tnoiMod=0 (b) tnoiMod=1

Noise source

NP5 controller Noise figure meter

HP8970 Noise figure test set

Network analyzer HP8510 DC power supply

HP4142

DUT

Mismatch Noise Source

(MNS)

Remote Receiver

Module (RRM)

MNS: A solid state electronic tuner with embedded bias-T and switching circuit.

RRM: A low noise amplifier with embedded bias-T and switching circuit.

Fig. 2.4 Block diagram of ATN NP5B noise figure measurement system configuration.

Chapter 3

Scalable Lossy Substrate Model for Various Pad Structures

The original lossy substrate model proved the mechanism of excess noises caused by substrate loss coupled through the lossy pads [4-6]. In this chapter, an enhanced lossy substrate model is developed to accurately simulate RF noise for on-chip devices with freedom in pad and TML (transmission line) layouts. This new lossy substrate model is composed of two parallel and series RLC networks in series with Cpad and Cox to model the capacitive coupling through the GSG pads and TML from the low resistivity silicon substrate.

The precise distribution of lossy substrate effect between that through the pads and the remaining portion through TML realizes accurate prediction of S-parameters and noise parameters for miniaturized devices over broadband regime.