Physical Modeling of Graphene Nanoribbon Field Effect Transistor Using Non-Equilibrium Green Function Approach for Integrated Circuit Design

The driving engine for the exponential growth of digital information processing systems is scaling down the transistor dimensions. For decades, this has enhanced the device performance and density. However, the International Technology Roadmap for Semiconductors (ITRS) states the end of Moore’s law in the next decade due to the scaling challenges of silicon-based CMOS electronics, e.g. extremely high power density. The forward-looking solutions are the utilization of emerging materials and devices for integrated circuits. The Ph.D. dissertation focuses on graphene, one atomic layer of carbon sheet, experimentally discovered in 2004. Since fabrication technology of emerging materials is still in early stages, transistor modeling has been playing an important role for evaluating futuristic graphene-based devices and circuits. The GNR FET has been simulated by solving a numerical quantum transport model based on self-consistent solution of the 3D Poisson equation and 1D Schrödinger equations within the non-equilibrium Green’s function (NEGF) formalism. The quantum transport model fully treats short channel-length electrostatic effects and the quantum tunneling effects, leading to the technology exploration of graphene nanoribbon field effect transistors (GNRFETs) for the future. A comprehensive study of static metrics and switching attributes of GNRFET has been presented including the performance dependence of device characteristics to the GNR width and the scaling of its channel length down to 2.5 nanometer. It has been found that increasing the GNR width deteriorate the off-state performance of the GNRFET, such that, narrower armchair GNRs improved the device robustness to short channel effects, leading to better off-state performance considering smaller off-current, larger ION/IOFF ratio, smaller subthreshold swing and smaller drain-induced barrier-lowering. The wider armchair GNRs allow the scaling of channel length and supply voltage resulting in better on-state performance such as higher drive current, smaller intrinsic gate-delay time and smaller power-delay product. In addition, the width-dependent characteristics of GNR FETs is investigated for two GNR semiconducting families (3p,0) and (3p+1,0). It has been found that the GNRs(3p+1,0) demonstrates superior off-state performance, while, on the other hand, GNRs(3p,0) shows superior on-state performance. Thus, GNRs(3p+1,0) are promising for low-power design, while GNRs(3p,0) indicate a more preferable attribute for high frequency applications

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

Phase Noise Analyses and Measurements in the Hybrid Memristor-CMOS Phase-Locked Loop Design and Devices Beyond Bulk CMOS

Phase-locked loop (PLLs) has been widely used in analog or mixed-signal integrated circuits. Since there is an increasing market for low noise and high speed devices, PLLs are being employed in communications. In this dissertation, we investigated phase noise, tuning range, jitter, and power performances in different architectures of PLL designs. More energy efficient devices such as memristor, graphene, transition metal di-chalcogenide (TMDC) materials and their respective transistors are introduced in the design phase-locked loop. Subsequently, we modeled phase noise of a CMOS phase-locked loop from the superposition of noises from its building blocks which comprises of a voltage-controlled oscillator, loop filter, frequency divider, phase-frequency detector, and the auxiliary input reference clock. Similarly, a linear time-invariant model that has additive noise sources in frequency domain is used to analyze the phase noise. The modeled phase noise results are further compared with the corresponding phase-locked loop designs in different n-well CMOS processes. With the scaling of CMOS technology and the increase of the electrical field, the problem of short channel effects (SCE) has become dominant, which causes decay in subthreshold slope (SS) and positive and negative shifts in the threshold voltages of nMOS and pMOS transistors, respectively. Various devices are proposed to continue extending Moore\u27s law and the roadmap in semiconductor industry. We employed tunnel field effect transistor owing to its better performance in terms of SS, leakage current, power consumption etc. Applying an appropriate bias voltage to the gate-source region of TFET causes the valence band to align with the conduction band and injecting the charge carriers. Similarly, under reverse bias, the two bands are misaligned and there is no injection of carriers. We implemented graphene TFET and MoS2 in PLL design and the results show improvements in phase noise, jitter, tuning range, and frequency of operation. In addition, the power consumption is greatly reduced due to the low supply voltage of tunnel field effect transistor