Low-power emerging memristive designs towards secure hardware systems for applications in internet of things

Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and in-memory computing (IMC), but there is a rising interest in using memristive technologies for security applications in the era of internet of things (IoT). In this review article, for achieving secure hardware systems in IoT, low-power design techniques based on emerging memristive technology for hardware security primitives/systems are presented. By reviewing the state-of-the-art in three highlighted memristive application areas, i.e. memristive non-volatile memory, memristive reconfigurable logic computing and memristive artificial intelligent computing, their application-level impacts on the novel implementations of secret key generation, crypto functions and machine learning attacks are explored, respectively. For the low-power security applications in IoT, it is essential to understand how to best realize cryptographic circuitry using memristive circuitries, and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security. This review article aims to help researchers to explore security solutions, to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs

Reliability-aware circuit design to mitigate impact of device defects and variability in emerging memristor-based applications

In the last decades, semiconductor industry has fostered a fast downscale in technology, propelling the large scale integration of CMOS-based systems. The benefits in miniaturization are numerous, highlighting faster switching frequency, lower voltage supply and higher device density. However, this aggressive scaling trend it has not been without challenges, such as leakage currents, yield reduction or the increase in the overall system power dissipation. New materials, changes in the device structures and new architectures are key to keep the miniaturization trend. It is foreseen that 2D integration will eventually come to an insurmountable physical and economic limit, in which new strategic directions are required, such as the development of new device structures, 3D architectures or heterogeneous systems that takes advantage of the best of different technologies, both the ones already consolidated as well as emergent ones that provide performance and efficiency improvements in applications. In this context, memristor arises as one of several candidates in the race to find suitable emergent devices. Memristor, a blend of the words memory and resistor, is a passive device postulated by Leon Chua in 1971. In contrast with the other fundamental passive elements, memristors have the distinctive feature of modifying their resistance according to the charge that passes through these devices, and remaining unaltered when charge no longer flows. Although when it appeared no physical device implementation was acknowledged, HP Labs claimed in 2008 the manufacture of the first real memristor. This milestone triggered an unexpectedly high research activity about memristors, both in searching new materials and structures as well as in potential applications. Nowadays, memristors are not only appreciated in memory systems by their nonvolatile storage properties, but in many other fields, such as digital computing, signal processing circuits, or non-conventional applications like neuromorphic computing or chaotic circuits. In spite of their promising features, memristors show a primarily downside: they show significant device variation and limited lifetime due degradation compared with other alternatives. This Thesis explores the challenges that memristor variation and malfunction imposes in potential applications. The main goal is to propose circuits and strategies that either avoid reliability problems or take advantage of them. Throughout a collection of scenarios in which reliability issues are present, their impact is studied by means of simulations. This thesis is contextualized and their objectives are exposed in Chapter 1. In Chapter 2 the memristor is introduced, at both conceptual and experimental levels, and different compact levels are presented to be later used in simulations. Chapter 3 deepens in the phenomena that causes the lack of reliability in memristors, and models that include these defects in simulations are provided. The rest of the Thesis covers different applications. Therefore, Chapter 4 exhibits nonvolatile memory systems, and specifically an online test method for faulty cells. Digital computing is presented in Chapter 5, where a solution for the yield reduction in logic operations due to memristors variability is proposed. Lastly, Chapter 6 reviews applications in the analog domain, and it focuses in the exploitation of results observed in faulty memristor-based interconnect mediums for chaotic systems synchronization purposes. Finally, the Thesis concludes in Chapter 7 along with perspectives about future work.Este trabajo desarrolla un novedoso dispositivo condensador basado en el uso de la nanotecnología. El dispositivo parte del concepto existente de metal-aislador-metal (MIM), pero en lugar de una capa aislante continua, se utilizan nanopartículas dieléctricas. Las nanopartículas son principalmente de óxido de silicio (sílice) y poliestireno (PS) y los valores de diámetro son 255nm y 295nm respectivamente. Las nanopartículas contribuyen a una alta relación superficie/volumen y están fácilmente disponibles a bajo costo. La tecnología de depósito desarrollada en este trabajo se basa en la técnica de electrospray, que es una tecnología de fabricación ascendente (bottom-up) que permite el procesamiento por lotes y logra un buen compromiso entre una gran superficie y un bajo tiempo de depósito. Con el objetivo de aumentar la superficie de depósito, la configuración de electrospray ha sido ajustada para permitir áreas de depósito de 1cm2 a 25cm2. El dispositivo fabricado, los llamados condensadores de metal aislante de nanopartículas (NP-MIM) ofrecen valores de capacidad más altos que un condensador convencional similar con una capa aislante continua. En el caso de los NP-MIM de sílice, se alcanza un factor de hasta 1000 de mejora de la capacidad, mientras que los NP-MIM de poliestireno exhibe una ganancia de capacidad en el rango de 11. Además, los NP-MIM de sílice muestran comportamientos capacitivos en específicos rangos de frecuencias que depende de la humedad y el grosor de la capa de nanopartículas, mientras que los NP-MIM de poliestireno siempre mantienen su comportamiento capacitivo. Los dispositivos fabricados se han caracterizado mediante medidas de microscopía electrónica de barrido (SEM) complementadas con perforaciones de haz de iones focalizados (FIB) para caracterizar la topografía de los NP-MIMs. Los dispositivos también se han caracterizado por medidas de espectroscopia de impedancia, a diferentes temperaturas y humedades. El origen de la capacitancia aumentada está asociado en parte a la humedad en las interfaces de las nanopartículas. Se ha desarrollado un modelo de un circuito basado en elementos distribuidos para ajustar y predecir el comportamiento eléctrico de los NP-MIMs. En resumen, esta tesis muestra el diseño, fabricación, caracterización y modelización de un nuevo y prometedor condensador nanopartículas metal-aislante-metal que puede abrir el camino al desarrollo de una nueva tecnología de supercondensadores MIM

Reliability-aware circuit design to mitigate impact of device defects and variability in emerging memristor-based applications

In the last decades, semiconductor industry has fostered a fast downscale in technology, propelling the large scale integration of CMOS-based systems. The benefits in miniaturization are numerous, highlighting faster switching frequency, lower voltage supply and higher device density. However, this aggressive scaling trend it has not been without challenges, such as leakage currents, yield reduction or the increase in the overall system power dissipation. New materials, changes in the device structures and new architectures are key to keep the miniaturization trend. It is foreseen that 2D integration will eventually come to an insurmountable physical and economic limit, in which new strategic directions are required, such as the development of new device structures, 3D architectures or heterogeneous systems that takes advantage of the best of different technologies, both the ones already consolidated as well as emergent ones that provide performance and efficiency improvements in applications. In this context, memristor arises as one of several candidates in the race to find suitable emergent devices. Memristor, a blend of the words memory and resistor, is a passive device postulated by Leon Chua in 1971. In contrast with the other fundamental passive elements, memristors have the distinctive feature of modifying their resistance according to the charge that passes through these devices, and remaining unaltered when charge no longer flows. Although when it appeared no physical device implementation was acknowledged, HP Labs claimed in 2008 the manufacture of the first real memristor. This milestone triggered an unexpectedly high research activity about memristors, both in searching new materials and structures as well as in potential applications. Nowadays, memristors are not only appreciated in memory systems by their nonvolatile storage properties, but in many other fields, such as digital computing, signal processing circuits, or non-conventional applications like neuromorphic computing or chaotic circuits. In spite of their promising features, memristors show a primarily downside: they show significant device variation and limited lifetime due degradation compared with other alternatives. This Thesis explores the challenges that memristor variation and malfunction imposes in potential applications. The main goal is to propose circuits and strategies that either avoid reliability problems or take advantage of them. Throughout a collection of scenarios in which reliability issues are present, their impact is studied by means of simulations. This thesis is contextualized and their objectives are exposed in Chapter 1. In Chapter 2 the memristor is introduced, at both conceptual and experimental levels, and different compact levels are presented to be later used in simulations. Chapter 3 deepens in the phenomena that causes the lack of reliability in memristors, and models that include these defects in simulations are provided. The rest of the Thesis covers different applications. Therefore, Chapter 4 exhibits nonvolatile memory systems, and specifically an online test method for faulty cells. Digital computing is presented in Chapter 5, where a solution for the yield reduction in logic operations due to memristors variability is proposed. Lastly, Chapter 6 reviews applications in the analog domain, and it focuses in the exploitation of results observed in faulty memristor-based interconnect mediums for chaotic systems synchronization purposes. Finally, the Thesis concludes in Chapter 7 along with perspectives about future work.Este trabajo desarrolla un novedoso dispositivo condensador basado en el uso de la nanotecnología. El dispositivo parte del concepto existente de metal-aislador-metal (MIM), pero en lugar de una capa aislante continua, se utilizan nanopartículas dieléctricas. Las nanopartículas son principalmente de óxido de silicio (sílice) y poliestireno (PS) y los valores de diámetro son 255nm y 295nm respectivamente. Las nanopartículas contribuyen a una alta relación superficie/volumen y están fácilmente disponibles a bajo costo. La tecnología de depósito desarrollada en este trabajo se basa en la técnica de electrospray, que es una tecnología de fabricación ascendente (bottom-up) que permite el procesamiento por lotes y logra un buen compromiso entre una gran superficie y un bajo tiempo de depósito. Con el objetivo de aumentar la superficie de depósito, la configuración de electrospray ha sido ajustada para permitir áreas de depósito de 1cm2 a 25cm2. El dispositivo fabricado, los llamados condensadores de metal aislante de nanopartículas (NP-MIM) ofrecen valores de capacidad más altos que un condensador convencional similar con una capa aislante continua. En el caso de los NP-MIM de sílice, se alcanza un factor de hasta 1000 de mejora de la capacidad, mientras que los NP-MIM de poliestireno exhibe una ganancia de capacidad en el rango de 11. Además, los NP-MIM de sílice muestran comportamientos capacitivos en específicos rangos de frecuencias que depende de la humedad y el grosor de la capa de nanopartículas, mientras que los NP-MIM de poliestireno siempre mantienen su comportamiento capacitivo. Los dispositivos fabricados se han caracterizado mediante medidas de microscopía electrónica de barrido (SEM) complementadas con perforaciones de haz de iones focalizados (FIB) para caracterizar la topografía de los NP-MIMs. Los dispositivos también se han caracterizado por medidas de espectroscopia de impedancia, a diferentes temperaturas y humedades. El origen de la capacitancia aumentada está asociado en parte a la humedad en las interfaces de las nanopartículas. Se ha desarrollado un modelo de un circuito basado en elementos distribuidos para ajustar y predecir el comportamiento eléctrico de los NP-MIMs. En resumen, esta tesis muestra el diseño, fabricación, caracterización y modelización de un nuevo y prometedor condensador nanopartículas metal-aislante-metal que puede abrir el camino al desarrollo de una nueva tecnología de supercondensadores MIM.Postprint (published version

Reliability-aware circuit design to mitigate impact of device defects and variability in emerging memristor-based applications

In the last decades, semiconductor industry has fostered a fast downscale in technology, propelling the large scale integration of CMOS-based systems. The benefits in miniaturization are numerous, highlighting faster switching frequency, lower voltage supply and higher device density. However, this aggressive scaling trend it has not been without challenges, such as leakage currents, yield reduction or the increase in the overall system power dissipation. New materials, changes in the device structures and new architectures are key to keep the miniaturization trend. It is foreseen that 2D integration will eventually come to an insurmountable physical and economic limit, in which new strategic directions are required, such as the development of new device structures, 3D architectures or heterogeneous systems that takes advantage of the best of different technologies, both the ones already consolidated as well as emergent ones that provide performance and efficiency improvements in applications. In this context, memristor arises as one of several candidates in the race to find suitable emergent devices. Memristor, a blend of the words memory and resistor, is a passive device postulated by Leon Chua in 1971. In contrast with the other fundamental passive elements, memristors have the distinctive feature of modifying their resistance according to the charge that passes through these devices, and remaining unaltered when charge no longer flows. Although when it appeared no physical device implementation was acknowledged, HP Labs claimed in 2008 the manufacture of the first real memristor. This milestone triggered an unexpectedly high research activity about memristors, both in searching new materials and structures as well as in potential applications. Nowadays, memristors are not only appreciated in memory systems by their nonvolatile storage properties, but in many other fields, such as digital computing, signal processing circuits, or non-conventional applications like neuromorphic computing or chaotic circuits. In spite of their promising features, memristors show a primarily downside: they show significant device variation and limited lifetime due degradation compared with other alternatives. This Thesis explores the challenges that memristor variation and malfunction imposes in potential applications. The main goal is to propose circuits and strategies that either avoid reliability problems or take advantage of them. Throughout a collection of scenarios in which reliability issues are present, their impact is studied by means of simulations. This thesis is contextualized and their objectives are exposed in Chapter 1. In Chapter 2 the memristor is introduced, at both conceptual and experimental levels, and different compact levels are presented to be later used in simulations. Chapter 3 deepens in the phenomena that causes the lack of reliability in memristors, and models that include these defects in simulations are provided. The rest of the Thesis covers different applications. Therefore, Chapter 4 exhibits nonvolatile memory systems, and specifically an online test method for faulty cells. Digital computing is presented in Chapter 5, where a solution for the yield reduction in logic operations due to memristors variability is proposed. Lastly, Chapter 6 reviews applications in the analog domain, and it focuses in the exploitation of results observed in faulty memristor-based interconnect mediums for chaotic systems synchronization purposes. Finally, the Thesis concludes in Chapter 7 along with perspectives about future work.Este trabajo desarrolla un novedoso dispositivo condensador basado en el uso de la nanotecnología. El dispositivo parte del concepto existente de metal-aislador-metal (MIM), pero en lugar de una capa aislante continua, se utilizan nanopartículas dieléctricas. Las nanopartículas son principalmente de óxido de silicio (sílice) y poliestireno (PS) y los valores de diámetro son 255nm y 295nm respectivamente. Las nanopartículas contribuyen a una alta relación superficie/volumen y están fácilmente disponibles a bajo costo. La tecnología de depósito desarrollada en este trabajo se basa en la técnica de electrospray, que es una tecnología de fabricación ascendente (bottom-up) que permite el procesamiento por lotes y logra un buen compromiso entre una gran superficie y un bajo tiempo de depósito. Con el objetivo de aumentar la superficie de depósito, la configuración de electrospray ha sido ajustada para permitir áreas de depósito de 1cm2 a 25cm2. El dispositivo fabricado, los llamados condensadores de metal aislante de nanopartículas (NP-MIM) ofrecen valores de capacidad más altos que un condensador convencional similar con una capa aislante continua. En el caso de los NP-MIM de sílice, se alcanza un factor de hasta 1000 de mejora de la capacidad, mientras que los NP-MIM de poliestireno exhibe una ganancia de capacidad en el rango de 11. Además, los NP-MIM de sílice muestran comportamientos capacitivos en específicos rangos de frecuencias que depende de la humedad y el grosor de la capa de nanopartículas, mientras que los NP-MIM de poliestireno siempre mantienen su comportamiento capacitivo. Los dispositivos fabricados se han caracterizado mediante medidas de microscopía electrónica de barrido (SEM) complementadas con perforaciones de haz de iones focalizados (FIB) para caracterizar la topografía de los NP-MIMs. Los dispositivos también se han caracterizado por medidas de espectroscopia de impedancia, a diferentes temperaturas y humedades. El origen de la capacitancia aumentada está asociado en parte a la humedad en las interfaces de las nanopartículas. Se ha desarrollado un modelo de un circuito basado en elementos distribuidos para ajustar y predecir el comportamiento eléctrico de los NP-MIMs. En resumen, esta tesis muestra el diseño, fabricación, caracterización y modelización de un nuevo y prometedor condensador nanopartículas metal-aislante-metal que puede abrir el camino al desarrollo de una nueva tecnología de supercondensadores MIM

Noise tailoring, noise annealing and external noise injection strategies in memristive Hopfield neural networks

The commercial introduction of a novel electronic device is often preceded by a lengthy material optimization phase devoted to the suppression of device noise as much as possible. The emergence of novel computing architectures, however, triggers a paradigm change in noise engineering, demonstrating that a non-suppressed, but properly tailored noise can be harvested as a computational resource in probabilistic computing schemes. Such strategy was recently realized on the hardware level in memristive Hopfield neural networks delivering fast and highly energy efficient optimization performance. Inspired by these achievements we perform a thorough analysis of simulated memristive Hopfield neural networks relying on realistic noise characteristics acquired on various memristive devices. These characteristics highlight the possibility of orders of magnitude variations in the noise level depending on the material choice as well as on the resistance state (and the corresponding active region volume) of the devices. Our simulations separate the effects of various device non-idealities on the operation of the Hopfield neural network by investigating the role of the programming accuracy, as well as the noise type and noise amplitude of the ON and OFF states. Relying on these results we propose optimized noise tailoring, noise annealing, and external noise injection strategies.Comment: 13 pages, 7 figure

Neuromorphic Computing with Resistive Switching Devices.

Resistive switches, commonly referred to as resistive memory (RRAM) devices and modeled as memristors, are an emerging nanoscale technology that can revolutionize data storage and computing approaches. Enabled by the advancement of nanoscale semiconductor fabrication and detailed understanding of the physical and chemical processes occurring at the atomic scale, resistive switches offer high speed, low-power, and extremely dense nonvolatile data storage. Further, the analog capabilities of resistive switching devices enables neuromorphic computing approaches which can achieve massively parallel computation with a power and area budget that is orders of magnitude lower than today’s conventional, digital approaches. This dissertation presents the investigation of tungsten oxide based resistive switching devices for use in neuromorphic computing applications. Device structure, fabrication, and integration are described and physical models are developed to describe the behavior of the devices. These models are used to develop array-scale simulations in support of neuromorphic computing approaches. Several signal processing algorithms are adapted for acceleration using arrays of resistive switches. Both simulation and experimental results are reported. Finally, guiding principles and proposals for future work are discussed.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116743/1/sheridp_1.pd

Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing

Machine learning, particularly in the form of deep learning, has driven most of the recent fundamental developments in artificial intelligence. Deep learning is based on computational models that are, to a certain extent, bio-inspired, as they rely on networks of connected simple computing units operating in parallel. Deep learning has been successfully applied in areas such as object/pattern recognition, speech and natural language processing, self-driving vehicles, intelligent self-diagnostics tools, autonomous robots, knowledgeable personal assistants, and monitoring. These successes have been mostly supported by three factors: availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. The approaching demise of Moore's law, and the consequent expected modest improvements in computing power that can be achieved by scaling, raise the question of whether the described progress will be slowed or halted due to hardware limitations. This paper reviews the case for a novel beyond CMOS hardware technology, memristors, as a potential solution for the implementation of power-efficient in-memory computing, deep learning accelerators, and spiking neural networks. Central themes are the reliance on non-von-Neumann computing architectures and the need for developing tailored learning and inference algorithms. To argue that lessons from biology can be useful in providing directions for further progress in artificial intelligence, we briefly discuss an example based reservoir computing. We conclude the review by speculating on the big picture view of future neuromorphic and brain-inspired computing systems.Comment: Keywords: memristor, neuromorphic, AI, deep learning, spiking neural networks, in-memory computin

A PUF based Lightweight Hardware Security Architecture for IoT

With an increasing number of hand-held electronics, gadgets, and other smart devices, data is present in a large number of platforms, thereby increasing the risk of security, privacy, and safety breach than ever before. Due to the extreme lightweight nature of these devices, commonly referred to as IoT or `Internet of Things\u27, providing any kind of security is prohibitive due to high overhead associated with any traditional and mathematically robust cryptographic techniques. Therefore, researchers have searched for alternative intuitive solutions for such devices. Hardware security, unlike traditional cryptography, can provide unique device-specific security solutions with little overhead, address vulnerability in hardware and, therefore, are attractive in this domain. As Moore\u27s law is almost at its end, different emerging devices are being explored more by researchers as they present opportunities to build better application-specific devices along with their challenges compared to CMOS technology. In this work, we have proposed emerging nanotechnology-based hardware security as a security solution for resource constrained IoT domain. Specifically, we have built two hardware security primitives i.e. physical unclonable function (PUF) and true random number generator (TRNG) and used these components as part of a security protocol proposed in this work as well. Both PUF and TRNG are built from metal-oxide memristors, an emerging nanoscale device and are generally lightweight compared to their CMOS counterparts in terms of area, power, and delay. Design challenges associated with designing these hardware security primitives and with memristive devices are properly addressed. Finally, a complete security protocol is proposed where all of these different pieces come together to provide a practical, robust, and device-specific security for resource-limited IoT systems