Can deep-sub-micron device noise be used as the basis for probabilistic neural computation?

This thesis explores the potential of probabilistic neural architectures for computation with future nanoscale Metal-Oxide-Semiconductor Field Effect Transistors (MOSFETs). In particular, the performance of a Continuous Restricted Boltzmann Machine {CRBM) implemented with generated noise of Random Telegraph Signal (RTS) and 1/ f form has been studied with reference to the 'typical' Gaussian implementation. In this study, a time domain RTS based noise analysis capability has been developed based upon future nanoscale MOSFETs, to represent the effect of nanoscale MOSFET noise on circuit implementation in particular the synaptic analogue multiplier which is subsequently used to implement stochastic behaviour of the CRBM. The result of this thesis indicates little degradation in performance from that of the typical Gaussian CRBM. Through simulation experiments, the CRBM with nanoscale MOSFET noise shows the ability to reconstruct training data, although it takes longer to converge to equilibrium. The results in this thesis do not prove that nanoscale MOSFET noise can be exploited in all contexts and with all data, for probabilistic computation. However, the result indicates, for the first time, that nanoscale MOSFET noise has the potential to be used for probabilistic neural computation hardware implementation. This thesis thus introduces a methodology for a form of technology-downstreaming and highlights the potential of probabilistic architecture for computation with future nanoscale MOSFETs

Analysis and comprehensive analytical modeling of statistical variations in subthreshold MOSFET's high frequency characteristics

In this research, the analysis of statistical variations in subthreshold MOSFET's high frequency characteristics defined in terms of gate capacitance and transition frequency, have been shown and the resulting comprehensive analytical models of such variations in terms of their variances have been proposed. Major imperfection in the physical level properties including random dopant fluctuation and effects of variations in MOSFET's manufacturing process, have been taken into account in the proposed analysis and modeling. The up to dated comprehensive analytical model of statistical variation in MOSFET's parameter has been used as the basis of analysis and modeling. The resulting models have been found to be both analytic and comprehensive as they are the precise mathematical expressions in terms of physical level variables of MOSFET. Furthermore, they have been verified at the nanometer level by using 65~nm level BSIM4 based benchmarks and have been found to be very accurate with smaller than 5 % average percentages of errors. Hence, the performed analysis gives the resulting models which have been found to be the potential mathematical tool for the statistical and variability aware analysis and design of subthreshold MOSFET based VHF circuits, systems and applications

ENHANCEMENT OF MARKOV RANDOM FIELD MECHANISM TO ACHIEVE FAULT-TOLERANCE IN NANOSCALE CIRCUIT DESIGN

As the MOSFET dimensions scale down towards nanoscale level, the reliability of circuits based on these devices decreases. Hence, designing reliable systems using these nano-devices is becoming challenging. Therefore, a mechanism has to be devised that can make the nanoscale systems perform reliably using unreliable circuit components. The solution is fault-tolerant circuit design. Markov Random Field (MRF) is an effective approach that achieves fault-tolerance in integrated circuit design. The previous research on this technique suffers from limitations at the design, simulation and implementation levels. As improvements, the MRF fault-tolerance rules have been validated for a practical circuit example. The simulation framework is extended from thermal to a combination of thermal and random telegraph signal (RTS) noise sources to provide a more rigorous noise environment for the simulation of circuits build on nanoscale technologies. Moreover, an architecture-level improvement has been proposed in the design of previous MRF gates. The redesigned MRF is termed as Improved-MRF. The CMOS, MRF and Improved-MRF designs were simulated under application of highly noisy inputs. On the basis of simulations conducted for several test circuits, it is found that Improved-MRF circuits are 400 whereas MRF circuits are only 10 times more noise-tolerant than the CMOS alternatives. The number of transistors, on the other hand increased from a factor of 9 to 15 from MRF to Improved-MRF respectively (as compared to the CMOS). Therefore, in order to provide a trade-off between reliability and the area overhead required for obtaining a fault-tolerant circuit, a novel parameter called as ‘Reliable Area Index’ (RAI) is introduced in this research work. The value of RAI exceeds around 1.3 and 40 times for MRF and Improved-MRF respectively as compared to CMOS design which makes Improved- MRF to be still 30 times more efficient circuit design than MRF in terms of maintaining a suitable trade-off between reliability and area-consumption of the circuit

Comprehensive Analytical Models of Random Variations in Subthreshold MOSFET’s High-Frequency Performances

Subthreshold MOSFET has been adopted in many low power VHF circuits/systems in which their performances are mainly determined by three major high-frequency characteristics of intrinsic subthreshold MOSFET, i.e., gate capacitance, transition frequency, and maximum frequency of oscillation. Unfortunately, the physical level imperfections and variations in manufacturing process of MOSFET cause random variations in MOSFET’s electrical characteristics including the aforesaid high-frequency ones which in turn cause the undesired variations in those subthreshold MOSFET-based VHF circuits/systems. As a result, the statistical/variability aware analysis and designing strategies must be adopted for handling these variations where the comprehensive analytical models of variations in those major high-frequency characteristics of subthreshold MOSFET have been found to be beneficial. Therefore, these comprehensive analytical models have been reviewed in this chapter where interesting related issues have also been discussed. Moreover, an improved model of variation in maximum frequency of oscillation has also been proposed

ENHANCEMENT OF MARKOV RANDOM FIELD MECHANISM TO ACHIEVE FAULT-TOLERANCE IN NANOSCALE CIRCUIT DESIGN

As the MOSFET dimensions scale down towards nanoscale level, the reliability of circuits based on these devices decreases. Hence, designing reliable systems using these nano-devices is becoming challenging. Therefore, a mechanism has to be devised that can make the nanoscale systems perform reliably using unreliable circuit components. The solution is fault-tolerant circuit design. Markov Random Field (MRF) is an effective approach that achieves fault-tolerance in integrated circuit design. The previous research on this technique suffers from limitations at the design, simulation and implementation levels. As improvements, the MRF fault-tolerance rules have been validated for a practical circuit example. The simulation framework is extended from thermal to a combination of thermal and random telegraph signal (RTS) noise sources to provide a more rigorous noise environment for the simulation of circuits build on nanoscale technologies. Moreover, an architecture-level improvement has been proposed in the design of previous MRF gates. The redesigned MRF is termed as Improved-MRF. The CMOS, MRF and Improved-MRF designs were simulated under application of highly noisy inputs. On the basis of simulations conducted for several test circuits, it is found that Improved-MRF circuits are 400 whereas MRF circuits are only 10 times more noise-tolerant than the CMOS alternatives. The number of transistors, on the other hand increased from a factor of 9 to 15 from MRF to Improved-MRF respectively (as compared to the CMOS). Therefore, in order to provide a trade-off between reliability and the area overhead required for obtaining a fault-tolerant circuit, a novel parameter called as ‘Reliable Area Index’ (RAI) is introduced in this research work. The value of RAI exceeds around 1.3 and 40 times for MRF and Improved-MRF respectively as compared to CMOS design which makes Improved- MRF to be still 30 times more efficient circuit design than MRF in terms of maintaining a suitable trade-off between reliability and area-consumption of the circuit

Design of Neuromemristive Systems for Visual Information Processing

Neuromemristive systems (NMSs) are brain-inspired, adaptive computer architectures based on emerging resistive memory technology (memristors). NMSs adopt a mixed-signal design approach with closely-coupled memory and processing, resulting in high area and energy efficiencies. Previous work suggests that NMSs could even supplant conventional architectures in niche application domains such as visual information processing. However, given the infancy of the field, there are still several obstacles impeding the transition of these systems from theory to practice. This dissertation advances the state of NMS research by addressing open design problems spanning circuit, architecture, and system levels. Novel synapse, neuron, and plasticity circuits are designed to reduce NMSs’ area and power consumption by using current-mode design techniques and exploiting device variability. Circuits are designed in a 45 nm CMOS process with memristor models based on multilevel (W/Ag-chalcogenide/W) and bistable (Ag/GeS2/W) device data. Higher-level behavioral, power, area, and variability models are ported into MATLAB to accelerate the overall simulation time. The circuits designed in this work are integrated into neural network architectures for visual information processing tasks, including feature detection, clustering, and classification. Networks in the NMSs are trained with novel stochastic learning algorithms that achieve 3.5 reduction in circuit area, reduced design complexity, and exhibit similar convergence properties compared to the least-mean-squares algorithm. This work also examines the effects of device-level variations on NMS performance, which has received limited attention in previous work. The impact of device variations is reduced with a partial on-chip training methodology that enables NMSs to be configured with relatively sophisticated algorithms (e.g. resilient backpropagation), while maximizing their area-accuracy tradeoff

Fault Modeling of Graphene Nanoribbon FET Logic Circuits

[EN] Due to the increasing defect rates in highly scaled complementary metal-oxide-semiconductor (CMOS) devices, and the emergence of alternative nanotechnology devices, reliability challenges are of growing importance. Understanding and controlling the fault mechanisms associated with new materials and structures for both transistors and interconnection is a key issue in novel nanodevices. The graphene nanoribbon field-effect transistor (GNR FET) has revealed itself as a promising technology to design emerging research logic circuits, because of its outstanding potential speed and power properties. This work presents a study of fault causes, mechanisms, and models at the device level, as well as their impact on logic circuits based on GNR FETs. From a literature review of fault causes and mechanisms, fault propagation was analyzed, and fault models were derived for device and logic circuit levels. This study may be helpful for the prevention of faults in the design process of graphene nanodevices. In addition, it can help in the design and evaluation of defect- and fault-tolerant nanoarchitectures based on graphene circuits. Results are compared with other emerging devices, such as carbon nanotube (CNT) FET and nanowire (NW) FET.This work was supported in part by the Spanish Government under the research project TIN2016-81075-R and by Primeros Proyectos de Investigacion (PAID-06-18), Vicerrectorado de Investigacion, Innovacion y Transferencia de la Universitat Politecnica de Valencia (UPV), under the project 200190032.Gil Tomás, DA.; Gracia-Morán, J.; Saiz-Adalid, L.; Gil, P. (2019). Fault Modeling of Graphene Nanoribbon FET Logic Circuits. Electronics. 8(8):1-18. https://doi.org/10.3390/electronics8080851S11888International Technology Roadmap for Semiconductors (ITRS) 2013http://www.itrs2.net/2013-itrs.htmlSchuegraf, K., Abraham, M. C., Brand, A., Naik, M., & Thakur, R. (2013). Semiconductor Logic Technology Innovation to Achieve Sub-10 nm Manufacturing. IEEE Journal of the Electron Devices Society, 1(3), 66-75. doi:10.1109/jeds.2013.2271582International Technology Roadmap for Semiconductors (ITRS) 2015https://bit.ly/2xiiT8PNovoselov, K. S. (2004). Electric Field Effect in Atomically Thin Carbon Films. Science, 306(5696), 666-669. doi:10.1126/science.1102896Geim, A. K., & Novoselov, K. S. (2007). The rise of graphene. Nature Materials, 6(3), 183-191. doi:10.1038/nmat1849Wu, Y., Farmer, D. B., Xia, F., & Avouris, P. (2013). Graphene Electronics: Materials, Devices, and Circuits. Proceedings of the IEEE, 101(7), 1620-1637. doi:10.1109/jproc.2013.2260311Choudhury, M. R., Youngki Yoon, Jing Guo, & Mohanram, K. (2011). Graphene Nanoribbon FETs: Technology Exploration for Performance and Reliability. IEEE Transactions on Nanotechnology, 10(4), 727-736. doi:10.1109/tnano.2010.2073718Avouris, P. (2010). Graphene: Electronic and Photonic Properties and Devices. Nano Letters, 10(11), 4285-4294. doi:10.1021/nl102824hBanadaki, Y. M., & Srivastava, A. (2015). Scaling Effects on Static Metrics and Switching Attributes of Graphene Nanoribbon FET for Emerging Technology. IEEE Transactions on Emerging Topics in Computing, 3(4), 458-469. doi:10.1109/tetc.2015.2445104Avouris, P., Chen, Z., & Perebeinos, V. (2007). Carbon-based electronics. Nature Nanotechnology, 2(10), 605-615. doi:10.1038/nnano.2007.300Banerjee, S. K., Register, L. F., Tutuc, E., Basu, D., Kim, S., Reddy, D., & MacDonald, A. H. (2010). Graphene for CMOS and Beyond CMOS Applications. Proceedings of the IEEE, 98(12), 2032-2046. doi:10.1109/jproc.2010.2064151Schwierz, F. (2013). Graphene Transistors: Status, Prospects, and Problems. Proceedings of the IEEE, 101(7), 1567-1584. doi:10.1109/jproc.2013.2257633Fregonese, S., Magallo, M., Maneux, C., Happy, H., & Zimmer, T. (2013). Scalable Electrical Compact Modeling for Graphene FET Transistors. IEEE Transactions on Nanotechnology, 12(4), 539-546. doi:10.1109/tnano.2013.2257832Chen, Y.-Y., Sangai, A., Rogachev, A., Gholipour, M., Iannaccone, G., Fiori, G., & Chen, D. (2015). A SPICE-Compatible Model of MOS-Type Graphene Nano-Ribbon Field-Effect Transistors Enabling Gate- and Circuit-Level Delay and Power Analysis Under Process Variation. IEEE Transactions on Nanotechnology, 14(6), 1068-1082. doi:10.1109/tnano.2015.2469647Ferrari, A. C., Bonaccorso, F., Fal’ko, V., Novoselov, K. S., Roche, S., Bøggild, P., … Pugno, N. (2015). Science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems. Nanoscale, 7(11), 4598-4810. doi:10.1039/c4nr01600aHong, A. J., Song, E. B., Yu, H. S., Allen, M. J., Kim, J., Fowler, J. D., … Wang, K. L. (2011). Graphene Flash Memory. ACS Nano, 5(10), 7812-7817. doi:10.1021/nn201809kJeng, S.-L., Lu, J.-C., & Wang, K. (2007). A Review of Reliability Research on Nanotechnology. IEEE Transactions on Reliability, 56(3), 401-410. doi:10.1109/tr.2007.903188Srinivasu, B., & Sridharan, K. (2017). A Transistor-Level Probabilistic Approach for Reliability Analysis of Arithmetic Circuits With Applications to Emerging Technologies. IEEE Transactions on Reliability, 66(2), 440-457. doi:10.1109/tr.2016.2642168Teixeira Franco, D., Naviner, J.-F., & Naviner, L. (2006). Yield and reliability issues in nanoelectronic technologies. annals of telecommunications - annales des télécommunications, 61(11-12), 1422-1457. doi:10.1007/bf03219903Lin, Y.-M., Jenkins, K. A., Valdes-Garcia, A., Small, J. P., Farmer, D. B., & Avouris, P. (2009). Operation of Graphene Transistors at Gigahertz Frequencies. Nano Letters, 9(1), 422-426. doi:10.1021/nl803316hLiao, L., Lin, Y.-C., Bao, M., Cheng, R., Bai, J., Liu, Y., … Duan, X. (2010). High-speed graphene transistors with a self-aligned nanowire gate. Nature, 467(7313), 305-308. doi:10.1038/nature09405Wang, X., Tabakman, S. M., & Dai, H. (2008). Atomic Layer Deposition of Metal Oxides on Pristine and Functionalized Graphene. Journal of the American Chemical Society, 130(26), 8152-8153. doi:10.1021/ja8023059Geim, A. K. (2009). Graphene: Status and Prospects. Science, 324(5934), 1530-1534. doi:10.1126/science.1158877Mistewicz, K., Nowak, M., Wrzalik, R., Śleziona, J., Wieczorek, J., & Guiseppi-Elie, A. (2016). Ultrasonic processing of SbSI nanowires for their application to gas sensors. Ultrasonics, 69, 67-73. doi:10.1016/j.ultras.2016.04.004Jesionek, M., Nowak, M., Mistewicz, K., Kępińska, M., Stróż, D., Bednarczyk, I., & Paszkiewicz, R. (2018). Sonochemical growth of nanomaterials in carbon nanotube. Ultrasonics, 83, 179-187. doi:10.1016/j.ultras.2017.03.014Chen, X., Seo, D. H., Seo, S., Chung, H., & Wong, H.-S. P. (2012). Graphene Interconnect Lifetime: A Reliability Analysis. IEEE Electron Device Letters, 33(11), 1604-1606. doi:10.1109/led.2012.2211564Wang, Z. F., Zheng, H., Shi, Q. W., & Chen, J. (2009). Emerging nanodevice paradigm. ACM Journal on Emerging Technologies in Computing Systems, 5(1), 1-19. doi:10.1145/1482613.1482616Dong, J., Xiang, G., Xiang-Yang, K., & Jia-Ming, L. (2007). Atomistic Failure Mechanism of Single Wall Carbon Nanotubes with Small Diameters. Chinese Physics Letters, 24(1), 165-168. doi:10.1088/0256-307x/24/1/045Bu, H., Chen, Y., Zou, M., Yi, H., Bi, K., & Ni, Z. (2009). Atomistic simulations of mechanical properties of graphene nanoribbons. Physics Letters A, 373(37), 3359-3362. doi:10.1016/j.physleta.2009.07.04

Stochastic Memory Devices for Security and Computing

With the widespread use of mobile computing and internet of things, secured communication and chip authentication have become extremely important. Hardware-based security concepts generally provide the best performance in terms of a good standard of security, low power consumption, and large-area density. In these concepts, the stochastic properties of nanoscale devices, such as the physical and geometrical variations of the process, are harnessed for true random number generators (TRNGs) and physical unclonable functions (PUFs). Emerging memory devices, such as resistive-switching memory (RRAM), phase-change memory (PCM), and spin-transfer torque magnetic memory (STT-MRAM), rely on a unique combination of physical mechanisms for transport and switching, thus appear to be an ideal source of entropy for TRNGs and PUFs. An overview of stochastic phenomena in memory devices and their use for developing security and computing primitives is provided. First, a broad classification of methods to generate true random numbers via the stochastic properties of nanoscale devices is presented. Then, practical implementations of stochastic TRNGs, such as hardware security and stochastic computing, are shown. Finally, future challenges to stochastic memory development are discussed

Variability-aware low-power techniques for nanoscale mixed-signal circuits.

New circuit design techniques that accommodate lower supply voltages necessary for portable systems need to be integrated into the semiconductor intellectual property (IP) core. Systems that once worked at 3.3 V or 2.5 V now need to work at 1.8 V or lower, without causing any performance degradation. Also, the fluctuation of device characteristics caused by process variation in nanometer technologies is seen as design yield loss. The numerous parasitic effects induced by layouts, especially for high-performance and high-speed circuits, pose a problem for IC design. Lack of exact layout information during circuit sizing leads to long design iterations involving time-consuming runs of complex tools. There is a strong need for low-power, high-performance, parasitic-aware and process-variation-tolerant circuit design. This dissertation proposes methodologies and techniques to achieve variability, power, performance, and parasitic-aware circuit designs. Three approaches are proposed: the single iteration automatic approach, the hybrid Monte Carlo and design of experiments (DOE) approach, and the corner-based approach. Widely used mixed-signal circuits such as analog-to-digital converter (ADC), voltage controlled oscillator (VCO), voltage level converter and active pixel sensor (APS) have been designed at nanoscale complementary metal oxide semiconductor (CMOS) and subjected to the proposed methodologies. The effectiveness of the proposed methodologies has been demonstrated through exhaustive simulations. Apart from these methodologies, the application of dual-oxide and dual-threshold techniques at circuit level in order to minimize power and leakage is also explored