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Physical Unclonable Functions (PUFs) have gained attention as a lightweight hardware security primitive. In particular, the SRAM-based PUF uses the unpredictable power-up value of the cells within an SRAM. Although these values should ideally be always the same within each SRAM to accomplish a correct PUF operation, this is often not the case, especially when factors like circuit aging are considered. While certain studies explore the effects of aging on SRAM PUFs, they often simplify the analysis. For instance, some studies assume that only Bias Temperature Instability (BTI) contributes to circuit degradation while others evaluate the overall degradation without accounting for the stochastic effects of aging on each individual cell. In this work, we first perform a detailed characterization of the nature of aging in SRAM PUFs, demonstrating that the impact of Non-Conductive Hot-Carrier Injection cannot be neglected. We also show that different cells degrade differently, highlighting the importance of accounting for the stochasticity of aging. After that, a method based on the Data Retention Voltage metric to select the cells with the most stable power-up response is introduced. Using these cells to generate the PUF identifier will result in a more stable response, and thus a better PUF performance.

Time-Dependent Variability (TDV) phenomena represent a serious concern for device and circuit reliability. To address the TDV impact at circuit level, Reliability-Aware Design (RAD) tools can be used by circuit designers to achieve more reliable circuits. However, this is not a straightforward task, since the development of RAD tools comprises several steps such as the characterization, modeling and simulation of TDV phenomena. Furthermore, in deeply-scaled CMOS technologies, TDV reveals a stochastic nature that can complicate those steps. In this invited paper, we review some of the main challenges that appear in each step of the flow towards the development of RAD tools, providing our solutions to them.

The origin of some Time-Dependent Variability phenomena in FET technologies has been attributed to the charge carrier trapping/de-trapping activity of individual defects present in devices. Although some models have been presented to describe these phenomena from a so- called defect-centric perspective, limited attention has been paid to the complex process that goes from the experimental data of the phenomena up to the final construction of the model and all its components, specifically the one that pertains to the time constant distribution. This paper presents a detailed strategy aimed at determining the defect time constant distribution, specifically tailored for small area devices, using data obtained from conventional characterization procedures.

In nowadays deeply scaled CMOS technologies, time-dependent variability effects have become important concerns for analog and digital circuit design. Transistor parameter shifts caused by Bias Temperature Instability and Random Telegraph Noise phenomena can lead to deviations of the circuit performance or even to its fatal failure. In this scenario extensive and accurate device characterization under several test conditions has become an unavoidable step towards trustworthy implementing the stochastic reliability models. In this paper, the statistical distributions of threshold voltage shifts in nanometric CMOS transistors will be studied at near threshold, nominal and accelerated aging conditions. Statistical modelling of RTN and BTI combined effects covering the full voltage range is presented. The results of this work suppose a complete modelling approach of BTI and RTN that can be applied in a wide range of voltages for reliability predictions.

In nowadays deeply scaled CMOS technologies, time-zero and time-dependent variability effects have become important concerns for analog and digital circuit design. For instance, transistor parameter shifts caused by Bias Temperature Instability and Hot-Carrier Injection phenomena can lead to progressive deviations of the circuit performance or even to its catastrophic failure. In this scenario, and to understand the effects of these variability sources, an extensive and accurate device characterization under several test conditions has become an unavoidable step towards trustworthy implementing the stochastic reliability models and simulation tools needed to achieve reliable integrated circuits. In this paper, the statistical distributions of threshold voltage shifts in nanometric CMOS transistors will be studied at nominal and accelerated aging conditions. To this end, a versatile transistor array chip and a flexible measurement setup have been used to reduce the required testing time to attainable values.

This article presents a new methodology to extract, at a given operation condition, the statistical distribution of the number of active defects that contribute to the observed device time-dependent variability, as well as their amplitude distribution. Unlike traditional approaches based on complex and time-consuming individual analysis of thousands of current traces, the proposed approach uses a simpler trace processing, since only the maximum and minimum values of the drain current during a given time interval are needed. Moreover, this extraction method can also estimate defects causing small current shifts, which can be very complex to identify by traditional means. Experimental data in a wide range of gate voltages, from near-threshold up to nominal operation conditions, are analyzed with the proposed methodology.

This paper presents an innovative and automated measurement setup for the characterization of variability effects in CMOS transistors using array-based integrated circuits (ICs), through which a better understanding of CMOS reliability could be attained. This setup addresses the issues that come with the need for a trustworthy statistical characterization of these effects: testing a very large number of devices accurately but, also, in a timely manner. The setup consists of software and hardware components that provide a user-friendly interface to perform the statistical characterization of CMOS transistors. Five different electrical tests, comprehending time-zero and time-dependent variability effects, can be carried out. Test preparation is, with the described setup, reduced to a few seconds. Moreover, smart parallelization techniques allow reducing the typically time-consuming aging characterization from months to days or even hours. The scope of this paper thus encompasses the methodology and practice of measurement of CMOS time-dependent variability, as well as the development of appropriate measurement systems and components used in efficiently generating and acquiring the necessary electrical signals.

In the past few years, Time-Dependent Variability has become a subject of growing concern in CMOS technologies. In particular, phenomena such as Bias Temperature Instability, Hot-Carrier Injection and Random Telegraph Noise can largely affect circuit reliability. It becomes therefore imperative to develop reliability-aware design tools to mitigate their impact on circuits. To this end, these phenomena must be first accurately characterized and modeled. And, since all these phenomena reveal a stochastic nature for deeply-scaled integration technologies, they must be characterized massively on devices to extract the probability distribution functions associated to their characteristic parameters. In this work, a complete methodology to characterize these phenomena experimentally, and then extract the necessary parameters to construct a Time-Dependent Variability model, is presented. This model can be used by a reliability simulator. © 2020 Elsevier B.V.

Random Telegraph Noise (RTN)has attracted increasing interest in the last years. This phenomenon introduces variability in the electrical properties of transistors, in particular in deeply-scaled CMOS technologies, which can cause performance degradation in circuits. In this work, the dependence of RTN parameters, namely current jump amplitude and emission and capture time constants, on the bias conditions, both VG and VD, has been studied on a set of devices, with a high granularity in a broad voltage range. The results obtained for the VG dependences corroborate previous works, but suggest a unique trend for all the devices in a VG range that goes from the near-threshold region up to voltages over the nominal operation bias. However, different trends have been observed in the parameters dependence for the case of VD. From the experimental data, the probabilities of occupation of the associated defects have been evaluated, pointing out large device-to-device dispersion in the VD dependences.