The beneficial role of noise in the performance of Hf-based memristors has been experimentally studied. The addition of an external gaussian noise to the bias circuitry positively impacts the memristors characteristics by increasing the OFF/ON resistances ratio. The known stochastic resonance effect has been observed, when changing the standard deviation of the noise. The influence of the additive noise on the memristor current-voltage characteristic and on the set and reset related parameters are also presented.

Bias Temperature Instability has become a critical issue for circuit reliability. This phenomenon has been found to have a stochastic and discrete nature in nanometer-scale CMOS technologies. To account for this random nature, massive experimental characterization is necessary so that the extracted model parameters are accurate enough. However, there is a lack of automated analysis tools for the extraction of the BTI parameters from the extensive amount of generated data in those massive characterization tests. In this paper, a novel algorithm that allows the precise and fully automated parameter extraction from experimental BTI recovery current traces is presented. This algorithm is based on the Maximum Likelihood Estimation principles, and is able to extract, in a robust and exact manner, the threshold voltage shifts and emission times associated to oxide trap emissions during BTI recovery, required to properly model the phenomenon.

Process variability and time-dependent variability have become major concerns in deeply-scaled technologies. Two of the most important time-dependent variability phenomena are Bias Temperature Instability (BTI) and Hot-Carrier Injection (HCI), which can critically shorten the lifetime of circuits. Both BTI and HCI reveal a discrete and stochastic behavior in the nanometer scale, and, while process variability has been extensively treated, there is a lack of design methodologies that address the joint impact of these two phenomena on circuits. In this work, an automated and time-efficient design methodology that takes into account both process and time-dependent variability is presented. This methodology is based on the utilization of lifetime-aware Pareto-Optimal Fronts (POFs). The POFs are generated with a multi-objective optimization algorithm linked to a stochastic simulator. Both the optimization algorithm and the simulator have been specifically tailored to reduce the computational cost of the accurate evaluation of the impact on a circuit of both sources of variability.

This paper characterizes experimentally the aging degradation experienced by two different 2.45 GHz power amplifier circuits of similar performance, implemented in a 65 nm CMOS technology. Results demonstrate the importance of the topology selection in order to guarantee robustness against aging effects, and thus the need to predict MOS parameter degradation during the design phase, accounting for the actual DC and RF operation conditions. For that purpose, we propose a semi-empirical compact model that, based on the RMS equivalent voltages at the transistor terminals during circuit operation, can provide an estimation of the aging degradation.

With nowadays nanometer-CMOS technologies, time-zero and time dependent variability effects (like BTI, CHI, RTN, TDDB, EM, etc) have turned into an even more serious threat to the desired performance of analog and digital integrated circuits. Statistical characterization and modelling of these variability effects entail large testing times, which are typically prohibitive, and huge amounts of data, which are complex to post process. This paper describes novel characterization techniques that overcome these limitations, that is, they are capable of statistically testing nanometer CMOS devices with much shorter completion times. To properly handle the massive amounts of data from characterization, new extraction methods, described in this paper as well, have been developed. With these methods, an accurate variability-aware device model is completed, which can subsequently be used in reliability-aware circuit design methodologies.

Variability is one of the main and critical challenges introduced by the continuous scaling in integrated technologies and the need for reliable ICs. In this regard, it is necessary to take into account time-zero (i.e., spatial or process variability) and time-dependent variability (i.e., aging). While process variability has been extensively treated, considerable efforts are currently being made to develop new simulation tools to evaluate the impact of aging, but very few works have been focused on reliability simulation for analog ICs. The models for the wear-out phenomena typically use the stress conditions of the analog circuit during its normal operation. However, many of the available solutions often miss the bi-directional link between stress and biasing and their changes over time and, therefore, accuracy losses occur while evaluating the impact of aging in the circuit performance. This paper proposes a new size-adaptive time-step algorithm to efficiently update the stress conditions in the reliability simulation of analog ICs. Compared to similar solutions, the work presented here is able to attain similar accuracy levels with lower CPU times.

Bias Temperature Instability (BTI) is one of the key causes of reliability degradations of nano-CMOS circuits. While the long-term impact of BTI has been studied since years, the short-term implications of BTI on circuits are unexplored. In fact, in physics short-term BTI effects, i.e. instantaneous (i.e. sub μs) frequency dependent processes, have been recently reported. In order to design circuits with guardbands that are safe for long-term and instantaneous effects, new aging models are required. We are presenting the first approach that in fact considers both long-term as well as instantaneous BTI effects. It can be employed for complex circuits at the micro-architecture level. Designing guardbands based upon our physical BTI model reduces the guardbands by 41% and thus allows for the development of more cost-effective yet reliable designs. We also revisit existing state-of-the-art aging mitigation techniques to investigate how they can be properly adapted to additionally account for instantaneous aging effects. Along with our BTI model this further reduces the guardbands by up to 59%.

With further scaling of nanometer CMOS technologies, yield and reliability become an increasing challenge. This paper reviews the most important phenomena affecting yield and reliability. For each effect, the basic physical mechanisms causing the effect and its impact on transistor parameters are described. Possible solutions to cope/handle with these effects on the design level are discussed as well.