(Invited) Reliability in Ultimate CMOS Compatible Devices

The evolution of the field effect transistor technology has led to the reduction of the device dimensions to a few tenths of nanometers or even smaller. As the devices' dimensions are reduced, the occurrence of short-channel effects becomes unavoidable and degrades, to some extent, the electrical characteristics of the devices even when multiple gate or nanowire technologies are applied. This effect contributes to the variability of the devices, and, as a consequence, to its reliability. In ultimate devices such as nanowire FETs, reliability is inherently important since small variations in the electrical parameters, e.g. the drain current or the threshold voltage, can significantly affect the device performance in a circuit application. Several reliability issues such as Self-Heating Effect (SHE), Negative-Bias Temperature Instabilities (NBTI), Low-Frequency Noise (LFN), Random Telegraph Noise (RTN), and Time-Dependent Dielectric Breakdown (TDDB) are geometry-dependent and become more important for short-channel devices. Most of these parameters are strongly affected by the occurrence of defects or traps in the interface between the active region and gate dielectric. Thus, the analysis of the quality of the semiconductor-dielectric interface has become increasingly important and can be performed through different techniques. Each of them demands different facilities and can deliver different information from DUTs. Low-Frequency Noise Several is one of the most complete analyses that can be performed in terms of reliability in ultimate devices, as it enables access to several parameters with respect to traps’ characteristics. By the fundamental shape of the drain current noise spectral density (SId) as a function of the frequency (f), it is possible to verify the presence of trap centers, which overwhelm the overall noise, such that SId presents a plateau followed by a 1/f2 decay with frequency increase, the so-called G-R noise, instead of the standard 1/f shape, as shown in Fig. 1 for junctionless transistors. Also, through some mathematical manipulation in the case of 1/f shape, one can extract the active trap density (Nit), its dependence on bias conditions (Fig. 2(A)), and the trap depth inside the dielectric layer (Fig. 2(B)). If structures with different geometrical characteristics, such as different dielectric thicknesses need to be compared, it is useful to calculate the Gate Noise Parameter (GNP), which enables a fair comparison between different samples. This parameter can also be used to indicate the nature of the energy distribution of traps. For samples with 1/f2 aspect, where G-R noise is dominant, the activation energy of the dominant trap center can also be extracted through the measurement of LFN as a function of the temperature by plotting the Arrhenius curve. However, the low-frequency noise characterization requires a specific setup, including an LFN analyzer, Low-Noise Amplifiers (LNAs), and, frequently, strategies to compensate for the instrumentation background noise. Besides that, there are some situations in which the determination of Nit through LFN is difficult, as in the case of NBTI occurrence, where traps are generated along time. Therefore, other techniques can be applied either to extract parameters not easily obtained through LFN or to avoid the use of a specific setup. In this case, it could also be possible to extract trap density through capacitance curves, where a C-V meter is needed, or even through current curves (ID) as a function of time (t) or gate bias (VG). The ID-t and ID-VG characteristics can be an alternative to estimate active trap density and its profile along the bandgap, for example. Through a charge-pumping-like method, a pulsed signal can be applied in the gate of a transistor. Thus, the drain current will follow the gate bias, and the rise and fall times of ID can be associated with the occupation and release of traps, allowing for an estimation of Nit. It is worth mentioning that the accuracy of this estimation is better for devices with larger total trap density (N0) as stated in Fig. 3. Finally, there are methods that permit the extraction of active trap density and its profile along bandgap directly through ID-VG curves. In long devices, the active trap density can be estimated through the behavior of subthreshold swing, which is directly correlated to Nit and, in some ultimate devices, such as junctionless nanowire transistors, the variation of the substrate bias permits extraction of Nit profile along the bandgap (Fig. 4). This is only possible because, in these devices, the surface potential is not pinned at twice the Fermi potential in the conduction regime as it occurs in inversion mode transistors. Figure 1