Identification and Rejuvenation of NBTI-Critical Logic Paths in Nanoscale Circuits

The Negative Bias Temperature Instability (NBTI) phenomenon is agreed to be one of the main reliability concerns in nanoscale circuits. It increases the threshold voltage of pMOS transistors, thus, slows down signal propagation along logic paths between flip-flops. NBTI may cause intermittent faults and, ultimately, the circuit’s permanent functional failures. In this paper, we propose an innovative NBTI mitigation approach by rejuvenating the nanoscale logic along NBTI-critical paths. The method is based on hierarchical identification of NBTI-critical paths and the generation of rejuvenation stimuli using an Evolutionary Algorithm. A new, fast, yet accurate model for computation of NBTI-induced delays at gate-level is developed. This model is based on intensive SPICE simulations of individual gates. The generated rejuvenation stimuli are used to drive those pMOS transistors to the recovery phase, which are the most critical for the NBTI-induced path delay. It is intended to apply the rejuvenation procedure to the circuit, as an execution overhead, periodically. Experimental results performed on a set of designs demonstrate reduction of NBTI-induced delays by up to two times with an execution overhead of 0.1 % or less. The proposed approach is aimed at extending the reliable lifetime of nanoelectronics

Ingress of threshold voltage-triggered hardware trojan in the modern FPGA fabric–detection methodology and mitigation

The ageing phenomenon of negative bias temperature instability (NBTI) continues to challenge the dynamic thermal management of modern FPGAs. Increased transistor density leads to thermal accumulation and propagates higher and non-uniform temperature variations across the FPGA. This aggravates the impact of NBTI on key PMOS transistor parameters such as threshold voltage and drain current. Where it ages the transistors, with a successive reduction in FPGA lifetime and reliability, it also challenges its security. The ingress of threshold voltage-triggered hardware Trojan, a stealthy and malicious electronic circuit, in the modern FPGA, is one such potential threat that could exploit NBTI and severely affect its performance. The development of an effective and efficient countermeasure against it is, therefore, highly critical. Accordingly, we present a comprehensive FPGA security scheme, comprising novel elements of hardware Trojan infection, detection, and mitigation, to protect FPGA applications against the hardware Trojan. Built around the threat model of a naval warship’s integrated self-protection system (ISPS), we propose a threshold voltage-triggered hardware Trojan that operates in a threshold voltage region of 0.45V to 0.998V, consuming ultra-low power (10.5nW), and remaining stealthy with an area overhead as low as 1.5% for a 28 nm technology node. The hardware Trojan detection sub-scheme provides a unique lightweight threshold voltage-aware sensor with a detection sensitivity of 0.251mV/nA. With fixed and dynamic ring oscillator-based sensor segments, the precise measurement of frequency and delay variations in response to shifts in the threshold voltage of a PMOS transistor is also proposed. Finally, the FPGA security scheme is reinforced with an online transistor dynamic scaling (OTDS) to mitigate the impact of hardware Trojan through run-time tolerant circuitry capable of identifying critical gates with worst-case drain current degradation

Cross-Layer Approaches for an Aging-Aware Design of Nanoscale Microprocessors

Thanks to aggressive scaling of transistor dimensions, computers have revolutionized our life. However, the increasing unreliability of devices fabricated in nanoscale technologies emerged as a major threat for the future success of computers. In particular, accelerated transistor aging is of great importance, as it reduces the lifetime of digital systems. This thesis addresses this challenge by proposing new methods to model, analyze and mitigate aging at microarchitecture-level and above

Sensor de performance para células de memória CMOS

Vivemos hoje em dia tempos em que quase tudo tem um pequeno componente eletrónico e por sua vez esse componente precisa de uma memória para guardar as suas instruções. Dentro dos vários tipos de memórias, as Complementary Metal Oxide Semiconductor (CMOS) são as que mais utilização têm nos circuitos integrados e, com o avançar da tecnologia a ficar cada vez com uma escala mais reduzida, faz com que os problemas de performance e fiabilidade sejam uma constante. Efeitos como o BTI (Bias Thermal Instability), TDDB (Time Dependent Dielectric Breakdown), HCI (Hot Carrier Injection), EM (Electromigration), ao longo do tempo vão deteriorando os parâmetros físicos dos transístores de efeito de campo (MOSFET), mudando as suas propriedades elétricas. Associado ao efeito de BTI podemos ter o efeito PBTI (Positive BTI), que afeta mais os transístores NMOS, e o efeito NBTI (Negative BTI), que afeta mais os transístores PMOS. Se para nanotecnologias até 32 nanómetros o efeito NBTI é dominante, para tecnologias mais baixas os 2 efeitos são igualmente importantes. Porém, existem ainda outras variações no desempenho que podem colocar em causa o bom funcionamento dos circuitos, como as variações de processo (P), tensão (V) e temperatura (T), ou considerando todas estas variações, e de uma forma genérica, PVTA (Process, Voltage, Temperature and Aging). Tendo como base as células de memória de acesso aleatório (RAM, Random Access Memory), em particular as memórias estáticas (SRAM, Static Random Access Memory) e dinâmicas (DRAM, Dynamic Random Access Memory) que possuem tempos de leitura e escrita precisos, estas ficam bastante expostas ao envelhecimento dos seus componentes e, consecutivamente, acontece um decréscimo na sua performance, resultando em transições mais lentas, que por sua vez fará com que existam leituras e escritas mais lentas e poderão ocorrer erros nessas leituras e escritas . Para além destes fenómenos, temos também o facto de a margem de sinal ruido (SNM - Static Noise Margin) diminuir, fazendo com que a fiabilidade da memória seja colocada em causa. O envelhecimento das memórias CMOS traduz-se, portanto, na ocorrência de erros nas memórias ao longo do tempo, o que é indesejável, especialmente em sistemas críticos onde a ocorrência de um erro ou uma falha na memória pode significar por em risco sistemas de elevada importância e fundamentais (por exemplo, em sistemas de segurança, um erro pode desencadear um conjunto de ações não desejadas). Anteriormente já foram apresentadas algumas soluções para esta monitorização dos erros de uma memória, disponíveis na literatura, como é o caso do sensor de envelhecimento embebido no circuito OCAS (On-Chip Aging Sensor), que permite detetar envelhecimento numa SRAM provocado pelo envelhecimento por NBTI. Contudo este sensor demonstra algumas limitações, pois apenas se aplica a um conjunto de células SRAM conectadas a uma bit line, não sendo aplicado individualmente a outras células de memória como uma DRAM e não contemplando o efeito PBTI. Outra solução apresentada anteriormente é o Sensor de Envelhecimento para Células de Memória CMOS que demonstra alguma evolução em relação ao sensor OCAS. Contudo, ainda tem limitações, como é o caso de estar bastante dependente do sincronismo com a memória e não permitir qualquer tipo de calibração do sistema ao longo do seu funcionamento. O trabalho apresentado nesta dissertação resolve muitos dos problemas existentes nos trabalhos anteriores. Isto é, apresenta-se um sensor de performance para memórias capaz de reconhecer quando é que a memória pode estar na eminência de falhar, devido a fatores que afetam o desempenho da memória nas operações de escrita e leitura. Ou seja, sinaliza de forma preditiva as falhas. Este sensor está dividido em três grandes partes, como a seguir se descreve. O Transistion Detector é uma delas, que funciona como um “conversor” das transições na bit line da memória para o sensor, criando pulsos de duração proporcional à duração da transição na bit line, sendo que uma transição rápida resulta em pulsos curtos e uma transição lenta resulta em pulsos longos. Esta parte do circuito apresenta 2 tipos de configurações para o caso de ser aplicado numa SRAM, sendo que uma das configurações é para as memórias SRAM inicializadas a VDD, e a segunda configuração para memórias SRAM inicializadas a VDD/2. É também apresentada uma terceira configuração para o caso de o detetor ser aplicado numa DRAM. O funcionamento do detetor de transições está baseado num conjunto de inversores desequilibrados (ou seja, com capacidades de condução diferentes entre o transístor N e P no inversor), criando assim inversores do tipo N (com o transístor N mais condutivo que o P) e inversores do tipo P (com o transístor P mais condutivo que o N) que respondem de forma diferente às transições de 1 para 0 e vice-versa. Estas diferenças serão cruciais para a criação do pulso final que entrará no Pulse Detetor. Este segundo bloco do sensor é responsável por carregar um condensador com uma tensão proporcional ao tempo que a bit line levou a transitar. É nesta parte que se apresenta uma caraterística nova e importante, quando comparado com as soluções já existentes, que é a capacidade do sensor poder ser calibrado. Para isso, é utilizado um conjunto de transístores para carregar o condensador durante o impulso gerado no detetor de transições, que permitem aumentar ou diminuir a resistência de carga do condensador, ficando este com mais ou menos tensão (a tensão proporcional ao tempo da transição da bit line) a ser usada na Comparação seguinte. O terceiro grande bloco deste sensor é resumidamente um bloco comparador, que compara a tensão guardada no condensador com uma tensão de referência disponível no sensor e definida durante o projeto. Este comparador tem a função de identificar qual destas 2 tensões é a mais alta (a do condensador, que é proporcional ao tempo de transição da bit line, ou a tensão de referência) e fazer com a mesma seja “disparada” para VDD, sendo que a tensão mais baixa será colocada a VSS. Desta forma é sinalizado se a transição que está a ser avaliada deve ser considerada um erro ou não. Para controlar todo o processo, o sensor tem na sua base de funcionamento um controlador (uma máquina de estados finita composta por 3 estados). O primeiro estado do controlador é o estado de Reset, que faz com que todos os pontos do circuito estejam com as tenções necessárias ao início de funcionamento do mesmo. O segundo estado é o Sample, que fica a aguardar uma transição na bit line para ser validada pelo sensor e fazer com que o mesmo avance para o terceiro estado, que é o de Compare, onde ativa o comparador do sensor e coloca no exterior o resultado dessa comparação. Assim, se for detetado uma transição demasiado lenta na bit line, que é um sinal de erro, o mesmo será sinalizado para o exterior activando o sinal de saída. Caso o sensor não detete nenhum erro nas transições, o sinal de saída não é activado. O sensor tem a capacidade de funcionar em modo on-line, ou seja, não é preciso desligar o circuito de memória do seu funcionamento normal para poder ser testado. Para além disso, pode ainda ser utilizado internamente na memória, como sensor local (monitorizando as células reais de memória), ou externamente, como sensor global, caso seja colocado a monitorizar uma célula de memória fictícia.Within the several types of memories, the Complementary Metal Oxide Semiconductor (CMOS) are the most used in the integrated circuits and, as technology advances and becomes increasingly smaller in scale, it makes performance and reliability a constant problem. Effects such as BTI (Bias Thermal Instability), the positive (PBTI - Positive BTI) and the negative (NBTI - Negative BTI), TDDB (Time Dependent Dielectric Breakdown), HCI (Hot Carrier Injection), EM (Electromigration), etc., are aging effects that contribute to a cumulatively degradation of the transistors. Moreover, other parametric variations may also jeopardize the proper functioning of circuits and contribute to reduce circuits’ performance, such as process variations (P), power-supply voltage variations (V) and temperature variations (T), or considering all these variations, and in a generic way, PVTA (Process, Voltage, Temperature and Aging). The Sensor proposed in this paper aims to signalize these problems so that the user knows when the memory operation may be compromised. The sensor is made up of three important parts, the Transition Detector, the Pulse Detector and the Comparator, creating a sensor that converts bit line transition created in a memory operation (read or write) into a pulse and a voltage, that can be compared with a reference voltage available in the sensor. If the reference voltage is higher than the voltage proportional to the bit line transition time, the sensor output is not activated; but if the bit line transition time is high enough to generate a voltage higher than the reference voltage in the sensor, the sensor output signalizes a predictive error, denoting that the memory performance is in a critical state that may lead to an error if corrective measures are not taken. One important feature in this sensor topology is that it can be calibrated during operation, by controlling sensor’s sensibility to the bit line transition. Another important feature is that it can be applied locally, to monitor the online operation of the memory, or globally, by monitoring a dummy memory in pre-defined conditions. Moreover, it can be applied to SRAM or DRAM, being the first online sensor available for DRAM memories

Simulation of charge-trapping in nano-scale MOSFETs in the presence of random-dopants-induced variability

The growing variability of electrical characteristics is a major issue associated with continuous downscaling of contemporary bulk MOSFETs. In addition, the operating conditions brought about by these same scaling trends have pushed MOSFET degradation mechanisms such as Bias Temperature Instability (BTI) to the forefront as a critical reliability threat. This thesis investigates the impact of this ageing phenomena, in conjunction with device variability, on key MOSFET electrical parameters. A three-dimensional drift-diffusion approximation is adopted as the simulation approach in this work, with random dopant fluctuations—the dominant source of statistical variability—included in the simulations. The testbed device is a realistic 35 nm physical gate length n-channel conventional bulk MOSFET. 1000 microscopically different implementations of the transistor are simulated and subjected to charge-trapping at the oxide interface. The statistical simulations reveal relatively rare but very large threshold voltage shifts, with magnitudes over 3 times than that predicted by the conventional theoretical approach. The physical origin of this effect is investigated in terms of the electrostatic influences of the random dopants and trapped charges on the channel electron concentration. Simulations with progressively increased trapped charge densities—emulating the characteristic condition of BTI degradation—result in further variability of the threshold voltage distribution. Weak correlations of the order of 10-2 are found between the pre-degradation threshold voltage and post-degradation threshold voltage shift distributions. The importance of accounting for random dopant fluctuations in the simulations is emphasised in order to obtain qualitative agreement between simulation results and published experimental measurements. Finally, the information gained from these device-level physical simulations is integrated into statistical compact models, making the information available to circuit designers

Runtime Monitoring for Dependable Hardware Design

Mit dem Voranschreiten der Technologieskalierung und der Globalisierung der Produktion von integrierten Schaltkreisen eröffnen sich eine Fülle von Schwachstellen bezüglich der Verlässlichkeit von Computerhardware. Jeder Mikrochip wird aufgrund von Produktionsschwankungen mit einem einzigartigen Charakter geboren, welcher sich durch seine Arbeitsbedingungen, Belastung und Umgebung in individueller Weise entwickelt. Daher sind deterministische Modelle, welche zur Entwurfszeit die Verlässlichkeit prognostizieren, nicht mehr ausreichend um Integrierte Schaltkreise mit Nanometertechnologie sinnvoll abbilden zu können. Der Bedarf einer Laufzeitanalyse des Zustandes steigt und mit ihm die notwendigen Maßnahmen zum Erhalt der Zuverlässigkeit. Transistoren sind anfällig für auslastungsbedingte Alterung, die die Laufzeit der Schaltung erhöht und mit ihr die Möglichkeit einer Fehlberechnung. Hinzu kommen spezielle Abläufe die das schnelle Altern des Chips befördern und somit seine zuverlässige Lebenszeit reduzieren. Zusätzlich können strahlungsbedingte Laufzeitfehler (Soft-Errors) des Chips abnormales Verhalten kritischer Systeme verursachen. Sowohl das Ausbreiten als auch das Maskieren dieser Fehler wiederum sind abhängig von der Arbeitslast des Systems. Fabrizierten Chips können ebenfalls vorsätzlich während der Produktion boshafte Schaltungen, sogenannte Hardwaretrojaner, hinzugefügt werden. Dies kompromittiert die Sicherheit des Chips. Da diese Art der Manipulation vor ihrer Aktivierung kaum zu erfassen ist, ist der Nachweis von Trojanern auf einem Chip direkt nach der Produktion extrem schwierig. Die Komplexität dieser Verlässlichkeitsprobleme machen ein einfaches Modellieren der Zuverlässigkeit und Gegenmaßnahmen ineffizient. Sie entsteht aufgrund verschiedener Quellen, eingeschlossen der Entwicklungsparameter (Technologie, Gerät, Schaltung und Architektur), der Herstellungsparameter, der Laufzeitauslastung und der Arbeitsumgebung. Dies motiviert das Erforschen von maschinellem Lernen und Laufzeitmethoden, welche potentiell mit dieser Komplexität arbeiten können. In dieser Arbeit stellen wir Lösungen vor, die in der Lage sind, eine verlässliche Ausführung von Computerhardware mit unterschiedlichem Laufzeitverhalten und Arbeitsbedingungen zu gewährleisten. Wir entwickelten Techniken des maschinellen Lernens um verschiedene Zuverlässigkeitseffekte zu modellieren, zu überwachen und auszugleichen. Verschiedene Lernmethoden werden genutzt, um günstige Überwachungspunkte zur Kontrolle der Arbeitsbelastung zu finden. Diese werden zusammen mit Zuverlässigkeitsmetriken, aufbauend auf Ausfallsicherheit und generellen Sicherheitsattributen, zum Erstellen von Vorhersagemodellen genutzt. Des Weiteren präsentieren wir eine kosten-optimierte Hardwaremonitorschaltung, welche die Überwachungspunkte zur Laufzeit auswertet. Im Gegensatz zum aktuellen Stand der Technik, welcher mikroarchitektonische Überwachungspunkte ausnutzt, evaluieren wir das Potential von Arbeitsbelastungscharakteristiken auf der Logikebene der zugrundeliegenden Hardware. Wir identifizieren verbesserte Features auf Logikebene um feingranulare Laufzeitüberwachung zu ermöglichen. Diese Logikanalyse wiederum hat verschiedene Stellschrauben um auf höhere Genauigkeit und niedrigeren Overhead zu optimieren. Wir untersuchten die Philosophie, Überwachungspunkte auf Logikebene mit Hilfe von Lernmethoden zu identifizieren und günstigen Monitore zu implementieren um eine adaptive Vorbeugung gegen statisches Altern, dynamisches Altern und strahlungsinduzierte Soft-Errors zu schaffen und zusätzlich die Aktivierung von Hardwaretrojanern zu erkennen. Diesbezüglich haben wir ein Vorhersagemodell entworfen, welches den Arbeitslasteinfluss auf alterungsbedingte Verschlechterungen des Chips mitverfolgt und dazu genutzt werden kann, dynamisch zur Laufzeit vorbeugende Techniken, wie Task-Mitigation, Spannungs- und Frequenzskalierung zu benutzen. Dieses Vorhersagemodell wurde in Software implementiert, welche verschiedene Arbeitslasten aufgrund ihrer Alterungswirkung einordnet. Um die Widerstandsfähigkeit gegenüber beschleunigter Alterung sicherzustellen, stellen wir eine Überwachungshardware vor, welche einen Teil der kritischen Flip-Flops beaufsichtigt, nach beschleunigter Alterung Ausschau hält und davor warnt, wenn ein zeitkritischer Pfad unter starker Alterungsbelastung steht. Wir geben die Implementierung einer Technik zum Reduzieren der durch das Ausführen spezifischer Subroutinen auftretenden Belastung von zeitkritischen Pfaden. Zusätzlich schlagen wir eine Technik zur Abschätzung von online Soft-Error-Schwachstellen von Speicherarrays und Logikkernen vor, welche auf der Überwachung einer kleinen Gruppe Flip-Flops des Entwurfs basiert. Des Weiteren haben wir eine Methode basierend auf Anomalieerkennung entwickelt, um Arbeitslastsignaturen von Hardwaretrojanern während deren Aktivierung zur Laufzeit zu erkennen und somit eine letzte Verteidigungslinie zu bilden. Basierend auf diesen Experimenten demonstriert diese Arbeit das Potential von fortgeschrittener Feature-Extraktion auf Logikebene und lernbasierter Vorhersage basierend auf Laufzeitdaten zur Verbesserung der Zuverlässigkeit von Harwareentwürfen

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Minimization of NBTI performance degradation using internal node control

Abstract—Negative Bias Temperature Instability (NBTI) is a significant reliability concern for nanoscale CMOS circuits. Its effects on circuit timing can be especially pronounced for circuits with standby-mode equipped functional units because these units can be subjected to static NBTI stress for extended periods of time. This paper proposes internal node control, in which the inputs to individual gates are directly manipulated to prevent this static NBTI fatigue. We give a mixed integer linear program formulation for an optimal solution to this problem. The optimal placement of internal node control yields an average 26.7 % reduction in NBTI-induced delay over a ten year period for the ISCAS85 benchmarks. We find that the problem is NP-complete and present a linear-time heuristic that can be used to quickly find near-optimal solutions. The heuristic solutions are, on average, within 0.17 % of optimal and all were within 0.60% of optimal. I

Techniques for Improving Security and Trustworthiness of Integrated Circuits

The integrated circuit (IC) development process is becoming increasingly vulnerable to malicious activities because untrusted parties could be involved in this IC development flow. There are four typical problems that impact the security and trustworthiness of ICs used in military, financial, transportation, or other critical systems: (i) Malicious inclusions and alterations, known as hardware Trojans, can be inserted into a design by modifying the design during GDSII development and fabrication. Hardware Trojans in ICs may cause malfunctions, lower the reliability of ICs, leak confidential information to adversaries or even destroy the system under specifically designed conditions. (ii) The number of circuit-related counterfeiting incidents reported by component manufacturers has increased significantly over the past few years with recycled ICs contributing the largest percentage of the total reported counterfeiting incidents. Since these recycled ICs have been used in the field before, the performance and reliability of such ICs has been degraded by aging effects and harsh recycling process. (iii) Reverse engineering (RE) is process of extracting a circuit’s gate-level netlist, and/or inferring its functionality. The RE causes threats to the design because attackers can steal and pirate a design (IP piracy), identify the device technology, or facilitate other hardware attacks. (iv) Traditional tools for uniquely identifying devices are vulnerable to non-invasive or invasive physical attacks. Securing the ID/key is of utmost importance since leakage of even a single device ID/key could be exploited by an adversary to hack other devices or produce pirated devices. In this work, we have developed a series of design and test methodologies to deal with these four challenging issues and thus enhance the security, trustworthiness and reliability of ICs. The techniques proposed in this thesis include: a path delay fingerprinting technique for detection of hardware Trojans, recycled ICs, and other types counterfeit ICs including remarked, overproduced, and cloned ICs with their unique identifiers; a Built-In Self-Authentication (BISA) technique to prevent hardware Trojan insertions by untrusted fabrication facilities; an efficient and secure split manufacturing via Obfuscated Built-In Self-Authentication (OBISA) technique to prevent reverse engineering by untrusted fabrication facilities; and a novel bit selection approach for obtaining the most reliable bits for SRAM-based physical unclonable function (PUF) across environmental conditions and silicon aging effects

Characterizationof FD-SOI transistor

In this project, measurements have been made on FD-SOI transistors, fabricated by CEA-LETI, to carry out a characterization of these devices, since they are very new and need to be studied. This work has focused on characterizing the aging mechanism of the devices and the observed RTN. To characterize the aging mechanism and variability of the samples based on the applied cycles, the measurements have been made by applying constant stress voltages (CVS) directly to the device with a wafer prove station and a semiconductor parameter analyzer (SPA). To observe TN, different electrical procedures have been studied, controlling the different parameters during the measurements.En aquest projecte s'han realitzat mesures en transistors FD-SOI, fabricats per CEA-LETI, per tal de dur a terme una caracterització d'aquests dispositius, ja que són molt nous i necessiten de ser estudiats. Aquest treball s'ha centrat en caracteritzar l'envelliment dels dispositius i el RTN observat. Per a caracteritzar l'envelliment i la variabilitat de les mostres en funció dels cicles aplicats, les mesures s'han realitzat aplicant tensions d'estrés constant (CVS) directament al dispositiu amb una taula de puntes i un analitzador de paràmetres de semiconductors (SPA). Per tal d'observar RTN s'han estudiat diferents procediments elèctrics, controlant els diferents paràmetres durant les mesures.En este proyecto se han realizado medidas en transistores FD-SOI, fabricados por CEA-LETI, para llevar a cabo una caracterización de estos dispositivos, puesto que son muy nuevos y necesitan de ser estudiados. Este trabajo se ha centrado en caracterizar los mecanismos de envejecimiento de los dispositivos y el RTN observado. Para caracterizar el envejecimiento y la variabilidad de las muestras en función de los ciclos aplicados, las medidas se han realizado aplicando tensiones de estrés constante (CVS) directamente al dispositivo con una tabla de puntas y un analizador de parámetros de semiconductores (SPA). Para observar RTN se han estudiado diferentes procedimientos eléctricos, controlando los diferentes parámetros durante las medidas