Hybrid low-voltage physical unclonable function based on inkjet-printed metal-oxide transistors

Modern society is striving for digital connectivity that demands information security. As an emerging technology, printed electronics is a key enabler for novel device types with free form factors, customizability, and the potential for large-area fabrication while being seamlessly integrated into our everyday environment. At present, information security is mainly based on software algorithms that use pseudo random numbers. In this regard, hardware-intrinsic security primitives, such as physical unclonable functions, are very promising to provide inherent security features comparable to biometrical data. Device-specific, random intrinsic variations are exploited to generate unique secure identifiers. Here, we introduce a hybrid physical unclonable function, combining silicon and printed electronics technologies, based on metal oxide thin film devices. Our system exploits the inherent randomness of printed materials due to surface roughness, film morphology and the resulting electrical characteristics. The security primitive provides high intrinsic variation, is non-volatile, scalable and exhibits nearly ideal uniqueness

DESIGN AUTOMATION FOR CARBON NANOTUBE CIRCUITS CONSIDERING PERFORMANCE AND SECURITY OPTIMIZATION

As prevailing copper interconnect technology advances to its fundamental physical limit, interconnect delay due to ever-increasing wire resistivity has greatly limited the circuit miniaturization. Carbon nanotube (CNT) interconnects have emerged as promising replacement materials for copper interconnects due to their superior conductivity. Buffer insertion for CNT interconnects is capable of improving circuit timing of signal nets with limited buffer deployment. However, due to the imperfection of fabricating long straight CNT, there exist significant unidimensional-spatially correlated variations on the critical CNT geometric parameters such as the diameter and density, which will affect the circuit performance. This dissertation develops a novel timing driven buffer insertion technique considering unidimensional correlations of variations of CNT. Although the fabrication variations of CNTs are not desired for the circuit designs targeting performance optimization and reliability, these inherent imperfections make them natural candidates for building highly secure physical unclonable function (PUF), which is an advanced hardware security technology. A novel CNT PUF design through leveraging Lorenz chaotic system is developed and we show that it is resistant to many machine learning modeling attacks. In summary, the studies in this dissertation demonstrate that CNT technology is highly promising for performance and security optimizations in advanced VLSI circuit design

Nano-intrinsic security primitives for internet of everything

With the advent of Internet-enabled electronic devices and mobile computer systems, maintaining data security is one of the most important challenges in modern civilization. The innovation of physically unclonable functions (PUFs) shows great potential for enabling low-cost low-power authentication, anti-counterfeiting and beyond on the semiconductor chips. This is because secrets in a PUF are hidden in the randomness of the physical properties of desirably identical devices, making it extremely difficult, if not impossible, to extract them. Hence, the basic idea of PUF is to take advantage of inevitable non-idealities in the physical domain to create a system that can provide an innovative way to secure device identities, sensitive information, and their communications. While the physical variation exists everywhere, various materials, systems, and technologies have been considered as the source of unpredictable physical device variation in large scales for generating security primitives. The purpose of this project is to develop emerging solid-state memory-based security primitives and examine their robustness as well as feasibility. Firstly, the author gives an extensive overview of PUFs. The rationality, classification, and application of PUF are discussed. To objectively compare the quality of PUFs, the author formulates important PUF properties and evaluation metrics. By reviewing previously proposed constructions ranging from conventional standard complementary metal-oxide-semiconductor (CMOS) components to emerging non-volatile memories, the quality of different PUFs classes are discussed and summarized. Through a comparative analysis, emerging non-volatile redox-based resistor memories (ReRAMs) have shown the potential as promising candidates for the next generation of low-cost, low-power, compact in size, and secure PUF. Next, the author presents novel approaches to build a PUF by utilizing concatenated two layers of ReRAM crossbar arrays. Upon concatenate two layers, the nonlinear structure is introduced, and this results in the improved uniformity and the avalanche characteristic of the proposed PUF. A group of cell readout method is employed, and it supports a massive pool of challenge-response pairs of the nonlinear ReRAM-based PUF. The non-linear PUF construction is experimentally assessed using the evaluation metrics, and the quality of randomness is verified using predictive analysis. Last but not least, random telegraph noise (RTN) is studied as a source of entropy for a true random number generation (TRNG). RTN is usually considered a disadvantageous feature in the conventional CMOS designs. However, in combination with appropriate readout scheme, RTN in ReRAM can be used as a novel technique to generate quality random numbers. The proposed differential readout-based design can maintain the quality of output by reducing the effect of the undesired noise from the whole system, while the controlling difficulty of the conventional readout method can be significantly reduced. This is advantageous as the differential readout circuit can embrace the resistance variation features of ReRAMs without extensive pre-calibration. The study in this thesis has the potential to enable the development of cost-efficient and lightweight security primitives that can be integrated into modern computer mobile systems and devices for providing a high level of security

Printed Electronics-Based Physically Unclonable Functions for Lightweight Security in the Internet of Things

Die moderne Gesellschaft strebt mehr denn je nach digitaler Konnektivität - überall und zu jeder Zeit - was zu Megatrends wie dem Internet der Dinge (Internet of Things, IoT) führt. Bereits heute kommunizieren und interagieren „Dinge“ autonom miteinander und werden in Netzwerken verwaltet. In Zukunft werden Menschen, Daten und Dinge miteinander verbunden sein, was auch als Internet von Allem (Internet of Everything, IoE) bezeichnet wird. Milliarden von Geräten werden in unserer täglichen Umgebung allgegenwärtig sein und über das Internet in Verbindung stehen. Als aufstrebende Technologie ist die gedruckte Elektronik (Printed Electronics, PE) ein Schlüsselelement für das IoE, indem sie neuartige Gerätetypen mit freien Formfaktoren, neuen Materialien auf einer Vielzahl von Substraten mit sich bringt, die flexibel, transparent und biologisch abbaubar sein können. Darüber hinaus ermöglicht PE neue Freiheitsgrade bei der Anpassbarkeit von Schaltkreisen sowie die kostengünstige und großflächige Herstellung am Einsatzort. Diese einzigartigen Eigenschaften von PE ergänzen herkömmliche Technologien auf Siliziumbasis. Additive Fertigungsprozesse ermöglichen die Realisierung von vielen zukunftsträchtigen Anwendungen wie intelligente Objekte, flexible Displays, Wearables im Gesundheitswesen, umweltfreundliche Elektronik, um einige zu nennen. Aus der Sicht des IoE ist die Integration und Verbindung von Milliarden heterogener Geräte und Systeme eine der größten zu lösenden Herausforderungen. Komplexe Hochleistungsgeräte interagieren mit hochspezialisierten, leichtgewichtigen elektronischen Geräten, wie z.B. Smartphones mit intelligenten Sensoren. Daten werden in der Regel kontinuierlich gemessen, gespeichert und mit benachbarten Geräten oder in der Cloud ausgetauscht. Dabei wirft die Fülle an gesammelten und verarbeiteten Daten Bedenken hinsichtlich des Datenschutzes und der Sicherheit auf. Herkömmliche kryptografische Operationen basieren typischerweise auf deterministischen Algorithmen, die eine hohe Schaltungs- und Systemkomplexität erfordern, was sie wiederum für viele leichtgewichtige Geräte ungeeignet macht. Es existieren viele Anwendungsbereiche, in denen keine komplexen kryptografischen Operationen erforderlich sind, wie z.B. bei der Geräteidentifikation und -authentifizierung. Dabei hängt das Sicherheitslevel hauptsächlich von der Qualität der Entropiequelle und der Vertrauenswürdigkeit der abgeleiteten Schlüssel ab. Statistische Eigenschaften wie die Einzigartigkeit (Uniqueness) der Schlüssel sind von großer Bedeutung, um einzelne Entitäten genau unterscheiden zu können. In den letzten Jahrzehnten hat die Hardware-intrinsische Sicherheit, insbesondere Physically Unclonable Functions (PUFs), eine große Strahlkraft hinsichtlich der Bereitstellung von Sicherheitsfunktionen für IoT-Geräte erlangt. PUFs verwenden ihre inhärenten Variationen, um gerätespezifische eindeutige Kennungen abzuleiten, die mit Fingerabdrücken in der Biometrie vergleichbar sind. Zu den größten Potenzialen dieser Technologie gehören die Verwendung einer echten Zufallsquelle, die Ableitung von Sicherheitsschlüsseln nach Bedarf sowie die inhärente Schlüsselspeicherung. In Kombination mit den einzigartigen Merkmalen der PE-Technologie werden neue Möglichkeiten eröffnet, um leichtgewichtige elektronische Geräte und Systeme abzusichern. Obwohl PE noch weit davon entfernt ist, so ausgereift und zuverlässig wie die Siliziumtechnologie zu sein, wird in dieser Arbeit gezeigt, dass PE-basierte PUFs vielversprechende Sicherheitsprimitiven für die Schlüsselgenerierung zur eindeutigen Geräteidentifikation im IoE sind. Dabei befasst sich diese Arbeit in erster Linie mit der Entwicklung, Untersuchung und Bewertung von PE-basierten PUFs, um Sicherheitsfunktionen für ressourcenbeschränkte gedruckte Geräte und Systeme bereitzustellen. Im ersten Beitrag dieser Arbeit stellen wir das skalierbare, auf gedruckter Elektronik basierende Differential Circuit PUF (DiffC-PUF) Design vor, um sichere Schlüssel für Sicherheitsanwendungen für ressourcenbeschränkte Geräte bereitzustellen. Die DiffC-PUF ist als hybride Systemarchitektur konzipiert, die siliziumbasierte und gedruckte Komponenten enthält. Es wird eine eingebettete PUF-Plattform entwickelt, um die Charakterisierung von siliziumbasierten und gedruckten PUF-Cores in großem Maßstab zu ermöglichen. Im zweiten Beitrag dieser Arbeit werden siliziumbasierte PUF-Cores auf Basis diskreter Komponenten hergestellt und statistische Tests unter realistischen Betriebsbedingungen durchgeführt. Eine umfassende experimentelle Analyse der PUF-Sicherheitsmetriken wird vorgestellt. Die Ergebnisse zeigen, dass die DiffC-PUF auf Siliziumbasis nahezu ideale Werte für die Uniqueness- und Reliability-Metriken aufweist. Darüber hinaus werden die Identifikationsfähigkeiten der DiffC-PUF untersucht, und es stellte sich heraus, dass zusätzliches Post-Processing die Identifizierbarkeit des Identifikationssystems weiter verbessern kann. Im dritten Beitrag dieser Arbeit wird zunächst ein Evaluierungsworkflow zur Simulation von DiffC-PUFs basierend auf gedruckter Elektronik vorgestellt, welche auch als Hybrid-PUFs bezeichnet werden. Hierbei wird eine Python-basierte Simulationsumgebung vorgestellt, welche es ermöglicht, die Eigenschaften und Variationen gedruckter PUF-Cores basierend auf Monte Carlo (MC) Simulationen zu untersuchen. Die Simulationsergebnisse zeigen, dass die Sicherheitsmetriken im besten Betriebspunkt nahezu ideal sind. Des Weiteren werden angefertigte PE-basierte PUF-Cores für statistische Tests unter verschiedenen Betriebsbedingungen, einschließlich Schwankungen der Umgebungstemperatur, der relativen Luftfeuchtigkeit und der Versorgungsspannung betrieben. Die experimentell bestimmten Resultate der Uniqueness-, Bit-Aliasing- und Uniformity-Metriken stimmen gut mit den Simulationsergebnissen überein. Der experimentell ermittelte durchschnittliche Reliability-Wert ist relativ niedrig, was durch die fehlende Passivierung und Einkapselung der gedruckten Transistoren erklärt werden kann. Die Untersuchung der Identifikationsfähigkeiten basierend auf den PUF-Responses zeigt, dass die Hybrid-PUF ohne zusätzliches Post-Processing nicht für kryptografische Anwendungen geeignet ist. Die Ergebnisse zeigen aber auch, dass sich die Hybrid-PUF zur Geräteidentifikation eignet. Der letzte Beitrag besteht darin, in die Perspektive eines Angreifers zu wechseln. Um die Sicherheitsfähigkeiten der Hybrid-PUF beurteilen zu können, wird eine umfassende Sicherheitsanalyse nach Art einer Kryptoanalyse durchgeführt. Die Analyse der Entropie der Hybrid-PUF zeigt, dass seine Anfälligkeit für Angriffe auf Modellbasis hauptsächlich von der eingesetzten Methode zur Generierung der PUF-Challenges abhängt. Darüber hinaus wird ein Angriffsmodell eingeführt, um die Leistung verschiedener mathematischer Klonangriffe auf der Grundlage von abgehörten Challenge-Response Pairs (CRPs) zu bewerten. Um die Hybrid-PUF zu klonen, wird ein Sortieralgorithmus eingeführt und mit häufig verwendeten Classifiers für überwachtes maschinelles Lernen (ML) verglichen, einschließlich logistischer Regression (LR), Random Forest (RF) sowie Multi-Layer Perceptron (MLP). Die Ergebnisse zeigen, dass die Hybrid-PUF anfällig für modellbasierte Angriffe ist. Der Sortieralgorithmus profitiert von kürzeren Trainingszeiten im Vergleich zu den ML-Algorithmen. Im Falle von fehlerhaft abgehörten CRPs übertreffen die ML-Algorithmen den Sortieralgorithmus

Emerging Technology Based Design of Primitives for Hardware Security

Hardware security concerns such as IP piracy and hardware Trojans have triggered research into circuit protection and malicious logic detection from various design perspectives. In this paper, emerging technologies are investigated by leveraging their unique properties for applications in the hardware security domain. Five example circuit structures including camouflaging gates, polymorphic gates, current/voltage based circuit protectors and current-based XOR logic are designed to prove the high efficiency of Silicon NanoWire FETs and Graphene SymFET in applications such as circuit protection and IP piracy prevention. Simulation results indicate that highly efficient and secure circuit structures can be achieved via the use of emerging technologies

Appropriateness of Imperfect CNFET Based Circuits for Error Resilient Computing Systems

With superior device performance consistently reported in extremely scaled dimensions, low dimensional materials (LDMs), including Carbon Nanotube Field Effect Transistor (CNFET) based technology, have shown the potential to outperform silicon for future transistors in advanced technology nodes. Studies have also demonstrated orders of magnitude improvement in energy efficiency possible with LDMs, in comparison to silicon at competing technology nodes. However, the current fabrication processes for these materials suffer from process imperfections and still appear to be inadequate to compete with silicon for the mainstream high volume manufacturing. Among the LDMs, CNFETs are the most widely studied and closest to high volume manufacturing. Recent works have shown a significant increase in the complexity of CNFET based systems, including demonstration of a 16-bit microprocessor. However, the design of such systems has involved significantly wider-than-usual transistors and avoidance of certain logic combinations. The resulting complexity of several thousand transistors in such systems is still far from the requirements of high-performance general-purpose computing systems having billions of transistors. With the current progress of the process to fabricate CNFETs, their introduction in mainstream manufacturing is expected to take several more years. For an earlier technology adoption, CNFETs appear to be suited for error-resilient computing systems where errors during computation can be tolerated to a certain degree. Such systems relax the need for precise circuits and a perfect process while leveraging the potential energy benefits of CNFET technology in comparison to conventional Si technology. In this thesis, we explore the potential applications using an imperfect CNFET process for error-resilient computing systems, including the impact of the process imperfections at the system level and methods to improve it. The current most widely adopted fabrication process for CNFETs (separation and placement of solution-based CNTs) still suffers from process imperfections, mainly from open CNTs due to missing of CNTs (in trenches connecting source and drain of CNFET). A fair evaluation of the performance of CNFET based circuits should thus take into consideration the effect of open CNTs, resulting in reduced drive currents. At the circuit level, this leads to failures in meeting 1) the minimum frequency requirement (due to an increase in critical path delay), and 2) the noise suppression requirement. We present a methodology to accurately capture the effect of open CNT imperfection in the state-of-the-art CNFET model, for circuit-level performance evaluation (both delay and glitch vulnerability) of CNFET based circuits using SPICE. A Monte Carlo simulation framework is also provided to investigate the statistical effect of open CNT imperfection on circuit-level performance. We introduce essential metrics to evaluate glitch vulnerability and also provide an effective link between glitch vulnerability and circuit topology. The past few years have observed significant growth of interest in approximate computing for a wide range of applications, including signal processing, data mining, machine learning, image, video processing, etc. In such applications, the result quality is not compromised appreciably, even in the presence of few errors during computation. The ability to tolerate few errors during computation relaxes the need to have precise circuits. Thus the approximate circuits can be designed, with lesser nodes, reduced stages, and reduced capacitance at few nodes. Consequently, the approximate circuits could reduce critical path delays and enhanced noise suppression in comparison to precise circuits. We present a systematic methodology utilizing Reduced Ordered Binary Decision Diagrams (ROBDD) for generating approximate circuits by taking an example of 16-bit parallel prefix CNFET adder. The approximate adder generated using the proposed algorithm has ~ 5x reduction in the average number of nodes failing glitch criteria (along paths to primary output) and 43.4% lesser Energy Delay Product (EDP) even at high open CNT imperfection, in comparison to the ideal case of no open CNT imperfection, at a mean relative error of 3.3%. The recent boom of deep learning has been made possible by VLSI technology advancement resulting in hardware systems, which can support deep learning algorithms. These hardware systems intend to satisfy the high-energy efficiency requirement of such algorithms. The hardware supporting such algorithms adopts neuromorphic-computing architectures with significantly less energy compared to traditional Von Neumann architectures. Deep Neural Networks (DNNs) belonging to deep learning domain find its use in a wide range of applications such as image classification, speech recognition, etc. Recent hardware systems have demonstrated the implementation of complex neural networks at significantly less power. However, the complexity of applications and depths of DNNs are expected to drastically increase in the future, imposing a demanding requirement in terms of scalability and energy efficiency of hardware technology. CNFET technology can be an excellent alternative to meet the aggressive energy efficiency requirement for future DNNs. However, degradation in circuit-level performance due to open CNT imperfection can result in timing failure, thus distorting the shape of non-linear activation function, leading to a significant degradation in classification accuracy. We present a framework to obtain sigmoid activation function considering the effect of open CNT imperfection. A digital neuron is explored to generate the sigmoid activation function, which deviates from the ideal case under imperfect process and reduced time period (increased clock frequency). The inherent error resilience of DNNs, on the other hand, can be utilized to mitigate the impact of imperfect process and maintain the shape of the activation function. We use pruning of synaptic weights, which, combined with the proposed approximate neuron, significantly reduces the chance of timing failures and helps to maintain the activation function shape even at high process imperfection and higher clock frequencies. We also provide a framework to obtain classification accuracy of Deep Belief Networks (class of DNNs based on unsupervised learning) using the activation functions obtained from SPICE simulations. By using both approximate neurons and pruning of synaptic weights, we achieve excellent system accuracy (only < 0.5% accuracy drop) with 25% improvement in speed, significant EDP advantage (56.7% less) even at high process imperfection, in comparison to a base configuration of the precise neuron and no pruning with the ideal process, at no area penalty. In conclusion, this thesis provides directions for the potential applicability of CNFET based technology for error-resilient computing systems. For this purpose, we present methodologies, which provide approaches to assess and design CNFET based circuits, considering process imperfections. We accomplish a DBN framework for digit recognition, considering activation functions from SPICE simulations incorporating process imperfections. We demonstrate the effectiveness of using approximate neuron and synaptic weight pruning to mitigate the impact of high process imperfection on system accuracy

Graphene and Beyond: Recent Advances in Two-Dimensional Materials Synthesis, Properties, and Devices

Since the isolation of graphene in 2004, two-dimensional (2D) materials research has rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of emergent applications. The unique 2D structure offers an open canvas to tailor and functionalize 2D materials through layer number, defects, morphology, moir\ue9 pattern, strain, and other control knobs. Through this review, we aim to highlight the most recent discoveries in the following topics: theory-guided synthesis for enhanced control of 2D morphologies, quality, yield, as well as insights toward novel 2D materials; defect engineering to control and understand the role of various defects, including in situ and ex situ methods; and properties and applications that are related to moir\ue9 engineering, strain engineering, and artificial intelligence. Finally, we also provide our perspective on the challenges and opportunities in this fascinating field