Traditionally, on-chip odometers have been utilized to monitor circuit aging for characterization or mitigation. However, modern applications, such as anticounterfeiting, demand odometers that not only track degradation but also accurately assess the circuit’s actual usage time and operating conditions. To meet these demands, the exploiting of the differing dynamics of key physical degradation mechanisms has been proposed. Unlike existing solutions, which require separate circuits for each degradation mechanism, this paper presents a more efficient approach. We introduce a novel ring oscillator-based odometer that takes advantage of the asymmetric stress of multiple degradation mechanisms. Central to this innovation is a newly developed processing block, the duty cycle-dependent frequency divider. This design enables the circuit to infer degradation data from two distinct mechanisms, positioning it as an ideal solution to monitor circuit usage over time.

Bias Temperature Instability (BTI) poses a significant challenge in ensuring the reliability of digital systems, affecting the delay of digital logic gates, which ultimately can lead into timing failures. Sophisticated defect-centric models have been developed and successfully calibrated against empirical data to forecast the impacts of BTI at the device level. However, their application to large-scale digital circuits operating under realistic workloads over typical system lifetimes is limited because of the computational complexity of defect-centric models. To make the application of aging models in that context feasible, a useful technique is to compress the transistor workloads into simplified and hence manageable representative workloads. While fast in terms of execution speed, previous techniques struggle with accuracy when predicting aging degradation, and can reach a very high average error in threshold voltage increase prediction. In this work, we review the compression techniques described in the literature and propose two novel approaches that surpass existing ones in terms of accuracy, which is demonstrated for a complex digital design used as benchmark. Specifically, our best compression technique matches the predictions obtained through the reference uncompressed workloads, introducing negligible error, and maintains low execution times to efficiently and accurately scale defect-centric models to the circuit level.

Silicon Physical Unclonable Functions (PUFs) have emerged as a promising solution for generating cryptographic keys in low-cost resource-constrained devices. A PUF is expected to be reliable, meaning that its response bits should remain consistent each time the corresponding challenges are queried. Unfortunately, the stability of these challenge-response pairs (CRPs) can be seriously eroded by environmental factors like temperature variations and the aging of the integrated circuits implementing the PUF. Several approaches, including bit masking, bit selection techniques, and error-correcting codes, have been proposed to obtain a reliable PUF operation in the face of temperature variations. As for aging, a new kind of aging-resilient silicon PUF has been reported that uses the time-varying phenomenon known as Random Telegraph Noise (RTN) as the underlying entropy source. Although this type of PUF preserves its reliability well when aged, it is not immune to the impact of temperature variations. The work presented here shows that it is possible to improve the thermal reliability of RTN-based PUFs with a proper combination of (a) a novel optimization-based bit selection technique, that is also applicable to other types of PUFs based on differential measurements; and (b) a temperature-aware tuning of the entropy-harvesting function.

PUFs have emerged as an alternative to traditional Non-Volatile Memories in the field of hardware security. In this paper, a novel PUF is proposed that uses the Random Telegraph Noise phenomenon as the underlying source of entropy. This phenomenon manifests as discrete and random shifts in the drain current of transistors and it is characterized by several parameters like the number of the defects in the device, as well as the emission and capture time constants and current shifts of these defects. Using the recently reported Maximum Current Fluctuation metric, it is possible to condense all this information and use it for the PUF design. By forming pairs of transistors, measuring, and comparing their Maximum Current Fluctuation over a given time interval, we demonstrate using numerical experiments that it is possible to obtain a PUF. Furthermore, the results reported here show that this RTN-based PUF matches, and even out-performs, other silicon PUFs in terms of uniqueness, unpredictability, and reliability with an evident advantage in silicon area.

A new strategy for millimeter-wave (mm-Wave) circuit and system synthesis, where the accuracy of electromagnetic simulations can be achieved in optimization-based design methodologies without sacrificing efficiency, is presented and tested within a real industrial project. This is done by properly partitioning the system, generating libraries of passive devices which are electromagnetically simulated prior to any circuit optimization, generating performance tradeoffs at different hierarchical levels with multiobjective optimization algorithms and hierarchically composing lower level sub-blocks. With this proposed solution, an entire mm-Wave system, from the passive component level up to the system level, has been designed and compared with the results obtained from a conventional design approach, demonstrating the outstanding capabilities of the methodology.

In recent years there has been a growing interest in electronic design automation methodologies for the optimization-based design of radio frequency (RF) circuits and systems. While for simple circuits several successful methodologies have been proposed, these very same methodologies exhibit significant deficiencies when the complexity of the circuit is increased. The majority of the published methodologies that can tackle RF systems are either based on high-level system specification tools or use models to estimate the system performances. Hence, such approaches do not usually provide the desired accuracy for RF systems. In this paper, a methodology based on hierarchical multilevel bottom-up design approaches is presented, where multiobjective optimization algorithms are used to design an entire RF system from the passive component level up to the system level. Furthermore, each level of the hierarchy is simulated with the highest accuracy possible: electromagnetic simulation accuracy at device-level and electrical simulations at circuit/system-level.

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.

In this paper, an analysis of the methodologies proposed in the past years to automate the synthesis of radio-frequency (RF) integrated circuit blocks is presented. In the light of this analysis, and to avoid nonsystematic iterations between sizing and layout design steps, a multiobjective optimization-based layout-aware sizing approach with preoptimized integrated inductor(s) design space is proposed. An automatic layout generation from netlist to ready-to-fabricate prototype is carried in-the-loop for each tentative sizing solution using an RF-specific module generator, template-based placer and evolutionary multinet router with preoptimized interconnect widths. The proposed approach exploits the full capabilities of the most established computer-aided design tools for RF design available nowadays, i.e., RF circuit simulator as performance evaluator, electromagnetic simulator for inductor characterization, and layout extractor to determine the complete circuit layout parasitics. Experiments are conducted over a widely used circuit in the RF context, showing the advantages of performing complete layout-aware sizing optimization from the very initial stages of the design process.

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.

This paper describes a class of real-life optimization problems that has not been addressed before: a multi-objective optimization in which one objective is neither minimized nor maximized but uniformly swept over a wide range. The limitations of conventional multi-objective optimization algorithms to deal with this kind of problems are illustrated via the optimization of radiofrequency inductors. For the first time, an algorithm is proposed that provides sets of solutions for this kind of problems.

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.

A new design methodology for radiofrequency circuits is presented that includes electromagnetic (EM) simulation of the inductors into the optimization flow. This is achieved by previously generating the Pareto-optimal front (POF) of the inductors using EM simulation. Inductors are selected from the Pareto front and their S-parameter matrix is included in the circuit netlist that is simulated using an RF simulator. Generating the EM-simulated POF of inductors is computationally expensive, but once generated, it can be used for any circuit design. The methodology is illustrated both for a singleobjective and a multiobjective optimization of a low noise amplifier.

The accuracy of high-frequency models of passive RF devices, e.g., inductors or transformers, presents one of the most challenging problems for RF integrated circuits. Accuracy limitations lead RF designers to time-consuming iterations with electromagnetic simulators. This paper will explore and compare two advanced modeling techniques. The first one is based on the segmented model approach, in which each device segment is characterized with a lumped element model. The second technique is based on the generation of surrogate models from the electromagnetic simulation of a set of device samples. Different modeling strategies (frequency separation, filtering according to self-resonance frequency, etc.) will be considered. Efficiency and accuracy of both, physical and surrogate, modeling techniques will be compared using a Si process technology.

Bottom-up synthesis approaches based on the hierarchical composition of performance models have been proposed as a promising alternative to conventional top-down hierarchical synthesis approaches. This paper discusses problems related to the context-dependence of performance models and proposes possible solutions. Techniques for the composition of multi-dimensional performance models so that the efficiency of the design space exploration is maximized are also discussed. An active filter is used to demonstrate the accuracy and efficiency of the techniques discussed here.

Emerging hierarchical design methodologies based on the use of Pareto-optimal fronts (PoFs) are promising candidates to reduce the bottleneck in the design of analog circuits. However, little work has been reported about how to transmit the information provided by the PoFs of low hierarchical level blocks through the hierarchy to compose the performance models of higher-level blocks. This composition actually poses several problems such as the dependence of the PoF performances on the surrounding circuitry and the complexity of dealing with multi-dimensional PoFs in order to explore more efficiently the design space. To deal with these problems, this paper proposes new mechanisms to represent and select candidate solutions from multi-dimensional PoFs that are transformed to the changing operating conditions enforced by the surrounding circuitry. These mechanisms are demonstrated with the generation of the performance model of an active filter by composing previously generated PoFs of operational amplifiers.

In this paper, a new methodology for the automated generation of the optimal performance trade-offs of integrated inductors is presented. The methodology combines a multiobjective optimization algorithm with electromagnetic simulation to get highly accurate results. A set of sized inductors is obtained showing the best performance trade-offs for a given technology. Unlike reported approaches for inductor synthesis, performance trade-offs are generated offline, i.e., before any specific inductance or quality factor are required. The tight efficiency versus accuracy trade-off of existing approaches is, in this way, avoided and performance evaluation via electromagnetic simulation becomes affordable.

In this paper, the application of multi-objective evolutionary algorithms to the evaluation of performance trade-offs of planar inductors, an almost ubiquitous device in radio-frequency microelectronics, is studied. The absence of appropriate stopping criteria in most evolutionary algorithms reveals to be critical in this application. A new stopping criterion based on monitoring a set of performance metrics that account for convergence and diversity is proposed and demonstrated with practical radio-frequency circuit design problems.

This article introduces an evolution-based methodology, named memetic single-objective evolutionary algorithm (MSOEA), for automated sizing of high-performance analog integrated circuits. Memetic algorithms may achieve higher global and local search ability by properly combining operators from different standard evolutionary algorithms. By integrating operators from the differential evolution algorithm, from the real-coded genetic algorithm, operators inspired by the simulated annealing algorithm, and a set of constraint handling techniques, MSOEA specializes in handling analog circuit design problems with numerous and tight design constraints. The method has been tested through the sizing of several analog circuits. The results show that design specifications are met and objective functions are highly optimized. Comparisons with available methods like genetic algorithm and differential evolution in conjunction with static penalty functions, as well as with intelligent selection-based differential evolution, are also carried out, showing that the proposed algorithm has important advantages in terms of constraint handling ability and optimization quality.

In analog integrated circuit design, iterations between electrical and physical syntheses to counterbalance layout-induced performance degradations should be avoided as much as possible. One possible solution involves the integration of traditionally separated electrical and physical synthesis phases by including layout-induced effects right into the electrical synthesis phase in what has been called parasitic-aware synthesis. This solution, as such, is not yet complete since there are geometric requirements (minimization of area or fulfillment of certain layout aspect ratio, among others) whose effects on the resulting parasitics are not usually considered during the electrical synthesis. In this paper, a layout-aware solution for analog cells that tackles both geometric and parasitic-aware electrical synthesis is proposed. Several design examples are provided.

This paper describes a programmable digital computing architecture dedicated to process information in accordance to the organization and operating principles of the four-layer neuron structure encountered at the visual system of locusts. This architecture takes advantage of the natural collision detection skills of locusts and is capable of processing images and ascertaining collision threats in real-time automotive scenarios. In addition to the locust features, the architecture embeds a topological feature estimator module to identify and classify objects in collision course

This paper introduces a new hierarchical analysis methodology which incorporates approximation strategies during the analysis process. Consequently, the circuit sizes that can be analyzed increase dramatically, without suffering from the combinatorial explosion of expression complexity. Moreover the interpretability and usability in practical applications is enabled by providing analytical models that keep complexity at a minimum with the prescribed accuracy.

Limitations of existing approaches for symbolic analysis of large analog circuits are discussed. To address their solution, a new methodology for hierarchical symbolic analysis is introduced. The combination of a hierarchical modeling technique and approximation strategies, comprising circuit reduction, graph-based symbolic solution of circuit equations and matrix-based error control, provides optimum results in terms of speech and quality of results.

The use of simplification before generation techniques to enable the approximate symbolic analysis of large analog circuits is discussed. This paper introduces an error control mechanism to drive the circuit reduction, which overcomes the accuracy problems of previous approaches. The features and efficiency of the new methodology are demonstrated through several practical examples.