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Low-power circuits are important for many applications, such as Internet of Things. Device variations and fluctuations are challenging their design. Random telegraph noise (RTN) is an important source of fluctuation. To verify a design by simulation, one needs assessing the impact of fluctuation in both driving current $\Delta {I}_{d}$ and threshold voltage $\Delta {V}_{\textsf {th}}$ . Many early works, however, only measured RTN-induced $\Delta {I}_{d}$ . $\Delta {V}_{\textsf {th}}$ was not directly measured because of two difficulties: its average value is low and it is highly dynamic. Early works often estimated $\Delta {V}_{\textsf {th}}$ from $\Delta {I}_{d}/{g}_{m}$ ( ${V}_{g} = {V}_{\textit {dd}}$ ), where ${g}_{m}$ is the transconductance, without giving its accuracy. The objective of this paper is to develop a new Trigger-When-Charged (TWC) technique for directly measuring the RTN-induced $\Delta {V}_{\textsf {th}}$ . By triggering the measurement only when a trap is charged, measurement accuracy is substantially improved. It is found that there is a poor correlation between $\Delta {I}_{d}/{g}_{m}$ ( ${V}_{g} = {V}_{\textit {dd}}$ ) and the directly measured $\Delta {V}_{\textsf {th}}$ ( ${V}_{g} = {V}_{\textsf {th}}$ ). The former is twice of the latter on average. The origin for this difference is analyzed. For the first time, the TWC is applied to evaluate device-to-device variations of the directly measured RTN-induced $\Delta {V}_{\textsf {th}}$ without selecting devices.

Negative bias temperature instability (NBTI)-generated defects (GDs) have been widely observed and known to play an important role in device’s lifetime. However, its characterization and modeling in nanoscaled devices is a challenge due to their stochastic nature. The objective of this paper is to develop a fast and accurate technique for characterizing the statistical properties of NBTI aging, which can be completed in one day and thus reduce test time significantly. The fast speed comes from replacing the conventional constant voltage stress by the voltage step stress (VSS), while the accuracy comes from capturing the GDs without recovery. The key advances are twofold: first, we demonstrate that this VSS-GD technique is applicable for nanoscaled devices; second, we verify the 15 accuracy of the statistical model based on the parameters extracted from this technique against independently measured data. The proposed method provides an effective solution for GD evaluation, as required when qualifying a CMOS process.

To predict the negative bias temperature instability (NBTI) towards the end of pMOSFETs’ 10 years lifetime, power-law based extrapolation is the industrial standard method. The prediction accuracy crucially depends on the accuracy of time exponents, n. The n reported by early work spreads in a wide range and varies with measurement conditions, which can lead to unacceptable errors when extrapolated to 10 years. The objective of this work is to find how to make the n extraction independent of measurement conditions. After removing the contribution from as-grown hole traps (AHT), a new method is proposed to capture the generated defects (GD) in their entirety. The n extracted by this method is around 0.2 and insensitive to measurement conditions for the four fabrication processes we tested. The model based on this method is verified by comparing its prediction with measurements. Under AC operation, the model predicts that GD can contribute to ~90% of NBTI at 10 years.

Power consumption is a key issue especially for the edge devices/units in an IoT system. Lowering operation voltage is an effective way to reduce power. As the overdrive voltage, Vg-Vth, becomes smaller, the device is more vulnerable to threshold voltage jitters. One source for the jitter is Random Telegraph Noises (RTN), which cause a fluctuation in both drain current, ΔId, and threshold voltage, ΔVth. Early works on RTN were focused on measuring ΔId and then evaluate ΔVth from ΔId/gm, where gm is transconductance. The accuracy of ΔVth obtained in this way is not known. The objective of this work is to assess its accuracy by comparing it with the ΔVth directly measured from pulse Id-Vg. It will be shown that the correlation between these two is poor, so that ΔVth must not be evaluated from ΔId/gm. This is caused by the device-specific localized current distribution near the threshold.

For nanometer MOSFETs, charging and discharging a single trap induces random telegraph noise (RTN). When there are more than a few traps, RTN signal becomes complex and appears as within a device fluctuation (WDF). RTN/WDF causes jitters in switch timing and is a major challenge to low power circuits. In addition to RTN/WDF, devices also age. The interaction between RTN/WDF and aging is of importance and not fully understood. Some researchers reported aging increasing RTN/WDF, while others showed RTN/WDF being hardly affected by aging. The objective of this paper is to investigate the impact of hot carrier aging (HCA) on the RTN/WDF of nMOSFETs. For devices of average RTN/WDF, it is found that the effect of HCA is generally modest. For devices of abnormally high RTN/WDF, however, for the first time, we report HCA reducing RTN/WDF substantially (>50%). This reduction originates from either a change of current distribution or defect losses.

MATLAB is widely used in all areas of applied mathematics, in education and research at universities, and in the industry. MATLAB stands for MATrixLABoratory and the software is built up around vectors and matrices. This makes the software particularly useful for linear algebra but MATLAB is also a great tool for solving algebraic and differential equations and for numerical integration. Among MATLAB software main function is to produce a calculator to calculate the Link Budget System and the noise of the system. A link budget is accounting of all of the gains and losses from the transmitter, through the medium used (i.e. free space, cable, waveguide, and fiber) to the receiver in a telecommunication system. The purpose of calculating link budget is to investigate the system performance tied to operation threshold, to get the minimum power, Cmin that should be received at the demodulator input in order for communication to work properly with reading typically of 10 dB.By using this design, all the parameter (i.e. Effective Isotropic Radiated Power (EIRP), Path loss/Free space path loss, Effective aperture, Flux density, Physical aperture, Transmit gain, Receive gain, Transmit power and Receive power) can be calculated. Lastly, the purpose of calculating noise in satellite link system is to get noise temperature ratio and noise density ratio. A noise temperature ratio is a figure of merit used to represent the quality of a satellite or an earth station receiver. While, noise density ratio is one of the most important and most often used parameters when evaluating a digital radio system.

Ion Sensitive Field-Effect Transistor (ISFET) is a kind of sensor that able to differentiate the ion by replacing the gate of the FET with electrode and the membrane. Membrane acts as selector for the ions whereas the sensor detects the ions and converts it into electrical signal. However, the sensor has weakness to detect main ion from the interfering ion in the mixed solution when the ions have same characteristic. In this work, potassium ion (K+) and ammonium ion (NH4+) was used as the sample for both ions that have similar size. To overcome the problem, the sensor needs to be trained for pre-calibrate and pre-process by developing a model of Artificial Neural Networks (ANN) in MATLAB software. The ANN makes the model learn the pattern by the sample of inputs and outputs to estimate results or to get more accurate data. Backpropagation is used as the learning method of ANN model. The objective of this work is to develop ANN model for ISFET sensor that able to estimate the main ion in mixed solution by learning the pattern of the input and output of the sensor. The ANN model performance can be optimized by altering certain parameters in the learning algorithm. The results show that the model is able to predict with 97% accuracy and has strong and precise estimation ability with R-factor of 91.55%.