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University of Minnesota

1. Delles, James. Non-Equilibrium Two-State Switching in Mesoscale, Ferromagnetic Particles.

Degree: PhD, Physics, 2019, University of Minnesota

There has been much theoretical study attempting to expand upon the Arrhenius law, f=fo exp(U/kT), which describes the switching rate in thermally activated, two-state systems, but few experiments to verify it. This is especially true for ferromagnetic particles. Most of the previous experiments performed attempting to study the Arrhenius law focus on the effect the Boltzmann factor, exp(U/kT), has on the switching rate since it dominates any measurement due to its exponential dependence on temperature. This has made it difficult to probe the underlying physics of the prefactor in front of the exponential. Using square, ferromagnetic particles of sizes 250 nm x 250 nm x 10 nm and 210 nm x 210 nm x 10 nm, controlling the barrier height using an applied field, and measuring the average dwell times in each individual state has allowed us to focus on these prefactors. Our measured prefactors vary by twenty five orders of magnitude, and they are smaller than those predicted by previous theories for particles of this size. They become so small as to reach unphysically short timescales. We attribute these unexpectedly small prefactors to our magnetic particles being multidomain and undergoing transitions before the particles have time to reach thermal equilibrium. We show that our particles have a higher probability of transitioning the less time they have been in a state which we attribute to the magnetization spending most of its time near the barrier allowing faster transitions.

Subjects/Keywords: Arrhenius; Ferromagnetism; Magnetodynamics; Magnetostatics; Mesoscale; RTN

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APA (6th Edition):

Delles, J. (2019). Non-Equilibrium Two-State Switching in Mesoscale, Ferromagnetic Particles. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206674

Chicago Manual of Style (16th Edition):

Delles, James. “Non-Equilibrium Two-State Switching in Mesoscale, Ferromagnetic Particles.” 2019. Doctoral Dissertation, University of Minnesota. Accessed October 28, 2020. http://hdl.handle.net/11299/206674.

MLA Handbook (7th Edition):

Delles, James. “Non-Equilibrium Two-State Switching in Mesoscale, Ferromagnetic Particles.” 2019. Web. 28 Oct 2020.

Vancouver:

Delles J. Non-Equilibrium Two-State Switching in Mesoscale, Ferromagnetic Particles. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2020 Oct 28]. Available from: http://hdl.handle.net/11299/206674.

Council of Science Editors:

Delles J. Non-Equilibrium Two-State Switching in Mesoscale, Ferromagnetic Particles. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206674


University of Waterloo

2. Malcolm, AJ. Multi-level Random Telegraph Noise Analysis Using Machine Learning Techniques.

Degree: 2020, University of Waterloo

RTN is a noise process which occurs in solid-state electrical devices such as MOSFETs and Josephson Junctions. Defects in the crystal structure of these devices trap charge carriers, resulting in modulations of the devices electrical transport properties such as mobility mu or threshold voltage Vth. This is observed as sudden transitions between two discrete current, voltage, or resistance levels, corresponding to the occupied/unoccupied states of the trap. The magnitude of a traps effects can be linked to its physical location in devices, and so we sought to apply RTN analysis during the characterization of devices such as MOSFETs to learn more about its structure and any trap dependence on temperature or bias levels. However, device measurements demonstrated a large proportion of RTN signals with more than two levels, which strongly suggests the presence of multiple charge defects. This scenario is commonly avoided in published research on RTN analysis, with most literature focusing on measurements showing the effects of only a single trap. The frequency with which multiple traps was observed in our measurements motivated the development of an algorithm to better characterize multi-level RTN, and to avoid discarding large swathes of measurement data. The developed algorithm applies mixture models formed with Gaussians to identify, isolate, and analyze RTN signals. This is accomplished through the use of machine learning techniques to maximize the likelihood that a constrained combination of these models describes the RTN components at every step. When multi-level RTN is present, it is further decomposed into its constituent components which allows characterization of each independent defects. The algorithm is applied to a set of cryogenic MOSFET measurements taken from near cutoff and into saturation, which demonstrates the ability to characterize trap count, trap amplitude, and state occupation distribution for each trap. Although development of the algorithm has precluded an in depth exploration of the effects of parameters such as temperature on these traps, that type of analysis could not have been achieved to a suitable level of accuracy without it.

Subjects/Keywords: random telegraph noise; rtn; machine learning; mosfet; cmos

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APA (6th Edition):

Malcolm, A. (2020). Multi-level Random Telegraph Noise Analysis Using Machine Learning Techniques. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16342

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Malcolm, AJ. “Multi-level Random Telegraph Noise Analysis Using Machine Learning Techniques.” 2020. Thesis, University of Waterloo. Accessed October 28, 2020. http://hdl.handle.net/10012/16342.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Malcolm, AJ. “Multi-level Random Telegraph Noise Analysis Using Machine Learning Techniques.” 2020. Web. 28 Oct 2020.

Vancouver:

Malcolm A. Multi-level Random Telegraph Noise Analysis Using Machine Learning Techniques. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2020 Oct 28]. Available from: http://hdl.handle.net/10012/16342.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Malcolm A. Multi-level Random Telegraph Noise Analysis Using Machine Learning Techniques. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16342

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

3. Tsiara, Artemisia. Electrical characterization & modeling of the trapping phenomena impacting the reliability of nanowire transistors for sub 10nm nodes : Caractérisations électriques et modélisation des phénomènes de piégeages affectant la fiabilité des technologies CMOS avancées (Nanofils) 10nm.

Degree: Docteur es, Nano electronique et nano technologies, 2019, Université Grenoble Alpes (ComUE)

Dans les technologies CMOS avancées, les défauts microscopiques localisées à l'interface Si (Nit) ou dans l'oxyde de grille (Nox) dégradent les performances des transistors CMOS, en augmentant le bruit de basse fréquence (LFN). Ces défauts sont généralement induits par le processus de fabrication ou par le vieillissement de l'appareil sous tension électrique (BTI, porteurs chauds). Dans des transistors canal SiGe ou III-V, leur densité est beaucoup plus élevé que dans le silicium et leur nature microscopique est encore inconnue. En outre, en sub 10 nm 3D comme nanofils, ces défauts répartis spatialement induisent des effets stochastiques typiques responsables de la "variabilité temporelle" de la performance de l'appareil. Cette nouvelle composante dynamique de la variabilité doit maintenant être envisagée en plus de la variabilité statique bien connu pour obtenir circuits fonctionnels et fiables. Aujourd'hui donc, il devient essentiel de bien comprendre les mécanismes de piégeage induites par ces défauts afin de concevoir et fabriquer des technologies CMOS robustes et fiables pour les nœuds de sub 10 nm.

In advanced CMOS technologies, microscopic defects localized at the Si interface (Nit) or within the gate oxide (Nox) degrade the performance of CMOS transistors, by increasing the low frequency noise (LFN). These defects are generally induced by the fabrication process or by the ageing of the device under electrical stress (BTI, Hot Carriers). In SiGe or III-V channel transistors, their density is much higher than in silicon and their microscopic nature still is unknown. In addition, in sub 10nm 3D like nanowires, these spatially distributed defects induce typical stochastic effects responsible for “temporal variability” of the device performance. This new dynamic variability component must now be considered in addition of the well-known static variability to obtain functional and reliable circuits. Therefore today it becomes essential to well understand the trapping mechanisms induced by these defects in order to design & fabricate robust and reliable CMOS technologies for sub 10nm nodes.

Advisors/Committee Members: Ghibaudo, Gérard (thesis director).

Subjects/Keywords: Nanofils; Bruit; Piégeages; CMOS avancés; Nanowires; Bti; Rtn; Traps; Advanced CMOS; 620

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tsiara, A. (2019). Electrical characterization & modeling of the trapping phenomena impacting the reliability of nanowire transistors for sub 10nm nodes : Caractérisations électriques et modélisation des phénomènes de piégeages affectant la fiabilité des technologies CMOS avancées (Nanofils) 10nm. (Doctoral Dissertation). Université Grenoble Alpes (ComUE). Retrieved from http://www.theses.fr/2019GREAT010

Chicago Manual of Style (16th Edition):

Tsiara, Artemisia. “Electrical characterization & modeling of the trapping phenomena impacting the reliability of nanowire transistors for sub 10nm nodes : Caractérisations électriques et modélisation des phénomènes de piégeages affectant la fiabilité des technologies CMOS avancées (Nanofils) 10nm.” 2019. Doctoral Dissertation, Université Grenoble Alpes (ComUE). Accessed October 28, 2020. http://www.theses.fr/2019GREAT010.

MLA Handbook (7th Edition):

Tsiara, Artemisia. “Electrical characterization & modeling of the trapping phenomena impacting the reliability of nanowire transistors for sub 10nm nodes : Caractérisations électriques et modélisation des phénomènes de piégeages affectant la fiabilité des technologies CMOS avancées (Nanofils) 10nm.” 2019. Web. 28 Oct 2020.

Vancouver:

Tsiara A. Electrical characterization & modeling of the trapping phenomena impacting the reliability of nanowire transistors for sub 10nm nodes : Caractérisations électriques et modélisation des phénomènes de piégeages affectant la fiabilité des technologies CMOS avancées (Nanofils) 10nm. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); 2019. [cited 2020 Oct 28]. Available from: http://www.theses.fr/2019GREAT010.

Council of Science Editors:

Tsiara A. Electrical characterization & modeling of the trapping phenomena impacting the reliability of nanowire transistors for sub 10nm nodes : Caractérisations électriques et modélisation des phénomènes de piégeages affectant la fiabilité des technologies CMOS avancées (Nanofils) 10nm. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); 2019. Available from: http://www.theses.fr/2019GREAT010

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