Leakage Power Reduction for Deeply-Scaled FinFET Circuits Operating in Multiple Voltage Regimes Using Fine-Grained Gate- Length Biasing Technique

Abstract-With the aggressive downscaling of the process technologies and importance of battery-powered systems, reducing leakage power consumption has become one of the most crucial design challenges for IC designers. This paper presents a devicecircuit cross-layer framework to utilize fine-grained gate-length biased FinFETs for circuit leakage power reduction in the near-and super-threshold operation regimes. The impacts of Gate-Length Biasing (GLB) on circuit speed and leakage power are first studied using one of the most advanced technology nodes -a 7nm FinFET technology. Then multiple standard cell libraries using different leakage reduction techniques, such as GLB and Dual-VT, are built in multiple operating regimes at this technology node. It is demonstrated that, compared to Dual-VT, GLB is a more suitable technique for the advanced 7nm FinFET technology due to its capability of delivering a finer-grained trade-off between the leakage power and circuit speed, not to mention the lower manufacturing cost. The circuit synthesis results of a variety of ISCAS benchmark circuits using the presented GLB 7nm FinFET cell libraries show up to 70% leakage improvement with zero degradation in circuit speed in the near-and super-threshold regimes, respectively, compared to the standard 7nm FinFET cell library

Design for Reliability and Low Power in Emerging Technologies

Die fortlaufende Verkleinerung von Transistor-Strukturgrößen ist einer der wichtigsten Antreiber für das Wachstum in der Halbleitertechnologiebranche. Seit Jahrzehnten erhöhen sich sowohl Integrationsdichte als auch Komplexität von Schaltkreisen und zeigen damit einen fortlaufenden Trend, der sich über alle modernen Fertigungsgrößen erstreckt. Bislang ging das Verkleinern von Transistoren mit einer Verringerung der Versorgungsspannung einher, was zu einer Reduktion der Leistungsaufnahme führte und damit eine gleichbleibenden Leistungsdichte sicherstellte. Doch mit dem Beginn von Strukturgrößen im Nanometerbreich verlangsamte sich die fortlaufende Skalierung. Viele Schwierigkeiten, sowie das Erreichen von physikalischen Grenzen in der Fertigung und Nicht-Idealitäten beim Skalieren der Versorgungsspannung, führten zu einer Zunahme der Leistungsdichte und, damit einhergehend, zu erschwerten Problemen bei der Sicherstellung der Zuverlässigkeit. Dazu zählen, unter anderem, Alterungseffekte in Transistoren sowie übermäßige Hitzeentwicklung, nicht zuletzt durch stärkeres Auftreten von Selbsterhitzungseffekten innerhalb der Transistoren. Damit solche Probleme die Zuverlässigkeit eines Schaltkreises nicht gefährden, werden die internen Signallaufzeiten üblicherweise sehr pessimistisch kalkuliert. Durch den so entstandenen zeitlichen Sicherheitsabstand wird die korrekte Funktionalität des Schaltkreises sichergestellt, allerdings auf Kosten der Performance. Alternativ kann die Zuverlässigkeit des Schaltkreises auch durch andere Techniken erhöht werden, wie zum Beispiel durch Null-Temperatur-Koeffizienten oder Approximate Computing. Wenngleich diese Techniken einen Großteil des üblichen zeitlichen Sicherheitsabstandes einsparen können, bergen sie dennoch weitere Konsequenzen und Kompromisse. Bleibende Herausforderungen bei der Skalierung von CMOS Technologien führen außerdem zu einem verstärkten Fokus auf vielversprechende Zukunftstechnologien. Ein Beispiel dafür ist der Negative Capacitance Field-Effect Transistor (NCFET), der eine beachtenswerte Leistungssteigerung gegenüber herkömmlichen FinFET Transistoren aufweist und diese in Zukunft ersetzen könnte. Des Weiteren setzen Entwickler von Schaltkreisen vermehrt auf komplexe, parallele Strukturen statt auf höhere Taktfrequenzen. Diese komplexen Modelle benötigen moderne Power-Management Techniken in allen Aspekten des Designs. Mit dem Auftreten von neuartigen Transistortechnologien (wie zum Beispiel NCFET) müssen diese Power-Management Techniken neu bewertet werden, da sich Abhängigkeiten und Verhältnismäßigkeiten ändern. Diese Arbeit präsentiert neue Herangehensweisen, sowohl zur Analyse als auch zur Modellierung der Zuverlässigkeit von Schaltkreisen, um zuvor genannte Herausforderungen auf mehreren Designebenen anzugehen. Diese Herangehensweisen unterteilen sich in konventionelle Techniken ((a), (b), (c) und (d)) und unkonventionelle Techniken ((e) und (f)), wie folgt: $\textbf{(a)}$ Analyse von Leistungszunahmen in Zusammenhang mit der Maximierung von Leistungseffizienz beim Betrieb nahe der Transistor Schwellspannung, insbesondere am optimalen Leistungspunkt. Das genaue Ermitteln eines solchen optimalen Leistungspunkts ist eine besondere Herausforderung bei Multicore Designs, da dieser sich mit den jeweiligen Optimierungszielsetzungen und der Arbeitsbelastung verschiebt. $\textbf{(b)}$ Aufzeigen versteckter Interdependenzen zwischen Alterungseffekten bei Transistoren und Schwankungen in der Versorgungsspannung durch „IR-drops“. Eine neuartige Technik wird vorgestellt, die sowohl Über- als auch Unterschätzungen bei der Ermittlung des zeitlichen Sicherheitsabstands vermeidet und folglich den kleinsten, dennoch ausreichenden Sicherheitsabstand ermittelt. $\textbf{(c)}$ Eindämmung von Alterungseffekten bei Transistoren durch „Graceful Approximation“, eine Technik zur Erhöhung der Taktfrequenz bei Bedarf. Der durch Alterungseffekte bedingte zeitlich Sicherheitsabstand wird durch Approximate Computing Techniken ersetzt. Des Weiteren wird Quantisierung verwendet um ausreichend Genauigkeit bei den Berechnungen zu gewährleisten. $\textbf{(d)}$ Eindämmung von temperaturabhängigen Verschlechterungen der Signallaufzeit durch den Betrieb nahe des Null-Temperatur Koeffizienten (N-ZTC). Der Betrieb bei N-ZTC minimiert temperaturbedingte Abweichungen der Performance und der Leistungsaufnahme. Qualitative und quantitative Vergleiche gegenüber dem traditionellen zeitlichen Sicherheitsabstand werden präsentiert. $\textbf{(e)}$ Modellierung von Power-Management Techniken für NCFET-basierte Prozessoren. Die NCFET Technologie hat einzigartige Eigenschaften, durch die herkömmliche Verfahren zur Spannungs- und Frequenzskalierungen zur Laufzeit (DVS/DVFS) suboptimale Ergebnisse erzielen. Dies erfordert NCFET-spezifische Power-Management Techniken, die in dieser Arbeit vorgestellt werden. $\textbf{(f)}$ Vorstellung eines neuartigen heterogenen Multicore Designs in NCFET Technologie. Das Design beinhaltet identische Kerne; Heterogenität entsteht durch die Anwendung der individuellen, optimalen Konfiguration der Kerne. Amdahls Gesetz wird erweitert, um neue system- und anwendungsspezifische Parameter abzudecken und die Vorzüge des neuen Designs aufzuzeigen. Die Auswertungen der vorgestellten Techniken werden mithilfe von Implementierungen und Simulationen auf Schaltkreisebene (gate-level) durchgeführt. Des Weiteren werden Simulatoren auf Systemebene (system-level) verwendet, um Multicore Designs zu implementieren und zu simulieren. Zur Validierung und Bewertung der Effektivität gegenüber dem Stand der Technik werden analytische, gate-level und system-level Simulationen herangezogen, die sowohl synthetische als auch reale Anwendungen betrachten

Impacto da variabilidade PVT em somadores construídos com XORs

A operação de soma é a mais usada em Unidades Lógicas e Aritméticas (ULA). A ULA é a unidade mais importante no processamento de dados. Em sistemas digitais, é desejado um somador completo com baixo consumo de energia e um alto desempenho. O somador completo faz parte do caminho crítico em sistemas computacionais, ele pode ser implementado de diversas maneiras, a maioria delas tendo como seu principal sub-circuito a porta lógica OU-exclusivo (XOR). Consequentemente, o estudo de somadores completos compostos por combinações de portas lógicas XOR é de grande valia para pesquisas na literatura. Melhorias nos módulos aritméticos pode reduzir significativamente o consumo de potência dos sistemas, mas em tecnologias nanométricas é necessário considerar o impacto da variabilidade. Esse trabalho tem como objetivo analisar projetos de somadores completos que quando submetidos aos efeitos de variabilidade devem ser robustos, ter um bom desempenho e mostrar bons resultados em consumo de energia, quando estão operando em tensão nominal e em tensão de quase limiar. Além disso, foi utilizada uma técnica chamada de célula de desacoplamento (Dcell) visando uma alternativa para a redução da variabilidade de processo. Esse trabalho analisa e compara 4 somadores tradicionais e 9 somadores completos construídos através de 3 blocos lógicos, dos quais 2 deles são substituídos por portas lógicas XOR, em uma tecnologia FinFET de 7nm. Foi observado que circuitos somadores que foram construídos usando a XOR da família lógica CMOS, especialmente no segundo bloco, obtiveram piores resultados de desempenho e consumo energético. Somadores operando em tensão nominal são cerca de 80% mais robustos quanto ao impacto da variabilidade de processo no consumo máximo. A operação em quase limiar implica em uma alta sensibilidade no desempenho e consumo, alcançando mais de 300% nos piores casos. Em relação à variabilidade de processo, foi verificado um aumento de sensibilidade de cerca de 40% no desempenho quando foram utilizadas a XOR V5 e a XOR V8 no segundo bloco dos somadores quando operando em tensão nominal. Para a operação em tensão de quase limiar o uso da metodologia proposta nesse trabalho mostrou ser uma boa opção para alcançar uma maior robustez quanto ao consumo dos circuitos. Considerando o uso da Dcell, na operação em tensão nominal, foi verificado uma redução no desempenho juntamente com uma redução na variabilidade. O melhor caso foi o somador FAV5V8 que para um aumento de 20% no atraso, obteve uma redução de 20% na variabilidade. Em relação ao consumo, houve uma redução de 16% na potência dinâmica, juntamente com uma redução de quase 30% na variabilidade, como o que ocorreu com o somador FAV8V1. Foi possível observar casos de redução da variabilidade em mais de 40% com um pequeno aumento no consumo dinâmico. O uso dessa técnica teve um alto impacto nos resultados de circuitos que operavam em tensão de quase limiar, chegando em alguns casos a mais de 40% de redução do desempenho para uma pequena redução na variabilidade. Quanto ao consumo, nesse caso, os somadores tradicionais foram os menos afetados, e novamente o uso da XOR V8 no segundo bloco para construção dos somadores mostrou ser uma boa opção para aumento da robustez dos circuitos.The sum operation is the most used in the Arithmetic and Logic Units (ALU). In digital systems, a complete adder with low energy consumption and high performance is desired. The full adder is part of the critical path in computer systems. It can be implemented in several ways, most of them having the OR-exclusive logic gate (XOR) as its main sub-circuit. Consequently, the study of full adders composed of combinations of XOR logic gates has a great value in the literature. Improvements in arithmetic modules can significantly reduce the power consumption of systems, however, in nanometric technologies it is necessary to consider the impact of variability. This work aims to analyse designs of full adders considering variability effects, comparing performance and energy consumption when operating at nominal voltage and also at near threshold voltage. In addition, a technique called decoupling cell (Dcell) was used to provide an alternative for reducing process variability. This work analyses and compares four traditional adders and nine adders built using three logic blocks, where two of them are replaced by XOR logic gates, in a 7nm FinFET technology. It was observed that full adders that were built using the XOR of the CMOS logic family, especially in the second block, had worse results in performance and energy consumption. Full adders operating at nominal voltage regime are about 80% more robust in terms of the impact of process variability on maximum consumption. The near threshold operation implies a high sensitivity in performance and consumption, reaching more than 300% in the worst cases. Regarding the process variability, there was an increase in sensitivity of about 40% in performance when the XOR V5 and XOR V8 were used in the second block of the adder when operating at nominal voltage. For the voltage operation of near threshold, the use of the methodology proposed in this work demonstrate to be a good option to achieve greater robustness regarding the consumption of the circuits. Considering the use of Dcell, in the operation at nominal voltage, a reduction in performance was verified together with a reduction in variability. The best case was the adder FAV5V8 which for a 20% increase in delay, obtained a reduction of 20% in variability. In relation to dynamic consumption, there was a 16% reduction in power, together with a reduction of almost 30% in variability, as occurred with the FAV8V1 adder. It was possible to observe cases of reduced variability by more than 40% with a small increase in dynamic consumption. The use of this technique had a high impact on the results of circuits operating at near threshold voltage, in some cases reaching more than 40% reduction in performance for a small reduction in variability. For consumption, in this case, the traditional full adders were the least affected, and again the use of the XOR V8 in the second block for the construction of the adder proved to be a good option for increasing the robustness of the circuits

Strain integration and performance optimization in sub-20nm FDSOI CMOS technology

La technologie CMOS à base de Silicium complètement déserté sur isolant (FDSOI) est considérée comme une option privilégiée pour les applications à faible consommation telles que les applications mobiles ou les objets connectés. Elle doit cela à son architecture garantissant un excellent comportement électrostatique des transistors ainsi qu'à l'intégration de canaux contraints améliorant la mobilité des porteurs. Ce travail de thèse explore des solutions innovantes en FDSOI pour nœuds 20nm et en deçà, comprenant l'ingénierie de la contrainte mécanique à travers des études sur les matériaux, les dispositifs, les procédés d'intégration et les dessins des circuits. Des simulations mécaniques, caractérisations physiques (µRaman), et intégrations expérimentales de canaux contraints (sSOI, SiGe) ou de procédés générant de la contrainte (nitrure, fluage de l'oxyde enterré) nous permettent d'apporter des recommandations pour la technologie et le dessin physique des transistors en FDSOI. Dans ce travail de thèse, nous avons étudié le transport dans les dispositifs à canal court, ce qui nous a amené à proposer une méthode originale pour extraire simultanément la mobilité des porteurs et la résistance d'accès. Nous mettons ainsi en évidence la sensibilité de la résistance d'accès à la contrainte que ce soit pour des transistors FDSOI ou nanofils. Nous mettons en évidence et modélisons la relaxation de la contrainte dans le SiGe apparaissant lors de la gravure des motifs et causant des effets géométriques (LLE) dans les technologies FDSOI avancées. Nous proposons des solutions de type dessin ainsi que des solutions technologiques afin d'améliorer la performance des cellules standard digitales et de mémoire vive statique (SRAM). En particulier, nous démontrons l'efficacité d'une isolation duale pour la gestion de la contrainte et l'extension de la capacité de polarisation arrière, qui un atout majeur de la technologie FDSOI. Enfin, la technologie 3D séquentielle rend possible la polarisation arrière en régime dynamique, à travers une co-optimisation dessin/technologie (DTCO).The Ultra-Thin Body and Buried oxide Fully Depleted Silicon On Insulator (UTBB FDSOI) CMOS technology has been demonstrated to be highly efficient for low power and low leakage applications such as mobile, internet of things or wearable. This is mainly due to the excellent electrostatics in the transistor and the successful integration of strained channel as a carrier mobility booster. This work explores scaling solutions of FDSOI for sub-20nm nodes, including innovative strain engineering, relying on material, device, process integration and circuit design layout studies. Thanks to mechanical simulations, physical characterizations and experimental integration of strained channels (sSOI, SiGe) and local stressors (nitride, oxide creeping, SiGe source/drain) into FDSOI CMOS transistors, we provide guidelines for technology and physical circuit design. In this PhD, we have in-depth studied the carrier transport in short devices, leading us to propose an original method to extract simultaneously the carrier mobility and the access resistance and to clearly evidence and extract the strain sensitivity of the access resistance, not only in FDSOI but also in strained nanowire transistors. Most of all, we evidence and model the patterning-induced SiGe strain relaxation, which is responsible for electrical Local Layout Effects (LLE) in advanced FDSOI transistors. Taking into account these geometrical effects observed at the nano-scale, we propose design and technology solutions to enhance Static Random Access Memory (SRAM) and digital standard cells performance and especially an original dual active isolation integration. Such a solution is not only stress-friendly but can also extend the powerful back-bias capability, which is a key differentiating feature of FDSOI. Eventually the 3D monolithic integration can also leverage planar Fully-Depleted devices by enabling dynamic back-bias owing to a Design/Technology Co-Optimization

로직 및 피지컬 합성에서의 타이밍 분석과 최적화

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 김태환.Timing analysis is one of the necessary steps in the development of a semiconductor circuit. In addition, it is increasingly important in the advanced process technologies due to various factors, including the increase of process–voltage–temperature variation. This dissertation addresses three problems related to timing analysis and optimization in logic and physical synthesis. Firstly, most static timing analysis today are based on conventional fixed flip-flop timing models, in which every flip-flop is assumed to have a fixed clock-to-Q delay. However, setup and hold skews affect the clock-to-Q delay in reality. In this dissertation, I propose a mathematical formulation to solve the problem and apply it to the clock skew scheduling problems as well as to the analysis of a given circuit, with a scalable speedup technique. Secondly, near-threshold computing is one of the promising concepts for energy-efficient operation of VLSI systems, but wide performance variation and nonlinearity to process variations block the proliferation. To cope with this, I propose a holistic hardware performance monitoring methodology for accurate timing prediction in a near-threshold voltage regime and advanced process technology. Lastly, an asynchronous circuit is one of the alternatives to the conventional synchronous style, and asynchronous pipeline circuit especially attractive because of its small design effort. This dissertation addresses the synthesis problem of lightening two-phase bundled-data asynchronous pipeline controllers, in which delay buffers are essential for guaranteeing the correct handshaking operation but incurs considerable area increase.타이밍 분석은 반도체 회로 개발 필수 과정 중 하나로, 최신 공정일수록 공정-전압-온도 변이 증가를 포함한 다양한 요인으로 하여금 그 중요성이 커지고 있다. 본 논문에서는 로직 및 피지컬 합성과 관련하여 세 가지 타이밍 분석 및 최적화 문제에 대해 다룬다. 첫째로, 오늘날 대부분의 정적 타이밍 분석은 모든 플립-플롭의 클럭-출력 딜레이가 고정된 값이라는 가정을 바탕으로 이루어졌다. 하지만 실제 클럭-출력 딜레이는 해당 플립-플롭의 셋업 및 홀드 스큐에 영향을 받는다. 본 논문에서는 이러한 특성을 수학적으로 정리하였으며, 이를 확장 가능한 속도 향상 기법과 더불어 주어진 회로의 타이밍 분석 및 클럭 스큐 스케쥴링 문제에 적용하였다. 둘째로, 유사 문턱 연산은 초고집적 회로 동작의 에너지 효율을 끌어 올릴 수 있다는 점에서 각광받지만, 큰 폭의 성능 변이 및 비선형성 때문에 널리 활용되고 있지 않다. 이를 해결하기 위해 유사 문턱 전압 영역 및 최신 공정 노드에서 보다 정확한 타이밍 예측을 위한 하드웨어 성능 모니터링 방법론 전반을 제안하였다. 마지막으로, 비동기 회로는 기존 동기 회로의 대안 중 하나로, 그 중에서도 비동기 파이프라인 회로는 비교적 적은 설계 노력만으로도 구현 가능하다는 장점이 있다. 본 논문에서는 2위상 묶음 데이터 프로토콜 기반 비동기 파이프라인 컨트롤러 상에서, 정확한 핸드셰이킹 통신을 위해 삽입된 딜레이 버퍼에 의한 면적 증가를 완화할 수 있는 합성 기법을 제시하였다.1 INTRODUCTION 1 1.1 Flexible Flip-Flop Timing Model 1 1.2 Hardware Performance Monitoring Methodology 4 1.3 Asynchronous Pipeline Controller 10 1.4 Contributions of this Dissertation 15 2 ANALYSIS AND OPTIMIZATION CONSIDERING FLEXIBLE FLIP-FLOP TIMING MODEL 17 2.1 Preliminaries 17 2.1.1 Terminologies 17 2.1.2 Timing Analysis 20 2.1.3 Clock-to-Q Delay Surface Modeling 21 2.2 Clock-to-Q Delay Interval Analysis 22 2.2.1 Derivation 23 2.2.2 Additional Constraints 26 2.2.3 Analysis: Finding Minimum Clock Period 28 2.2.4 Optimization: Clock Skew Scheduling 30 2.2.5 Scalable Speedup Technique 33 2.3 Experimental Results 37 2.3.1 Application to Minimum Clock Period Finding 37 2.3.2 Application to Clock Skew Scheduling 39 2.3.3 Efficacy of Scalable Speedup Technique 43 2.4 Summary 44 3 HARDWARE PERFORMANCE MONITORING METHODOLOGY AT NTC AND ADVANCED TECHNOLOGY NODE 45 3.1 Overall Flow of Proposed HPM Methodology 45 3.2 Prerequisites to HPM Methodology 47 3.2.1 BEOL Process Variation Modeling 47 3.2.2 Surrogate Model Preparation 49 3.3 HPM Methodology: Design Phase 52 3.3.1 HPM2PV Model Construction 52 3.3.2 Optimization of Monitoring Circuits Configuration 54 3.3.3 PV2CPT Model Construction 58 3.4 HPM Methodology: Post-Silicon Phase 60 3.4.1 Transfer Learning in Silicon Characterization Step 60 3.4.2 Procedures in Volume Production Phase 61 3.5 Experimental Results 62 3.5.1 Experimental Setup 62 3.5.2 Exploration of Monitoring Circuits Configuration 64 3.5.3 Effectiveness of Monitoring Circuits Optimization 66 3.5.4 Considering BEOL PVs and Uncertainty Learning 68 3.5.5 Comparison among Different Prediction Flows 69 3.5.6 Effectiveness of Prediction Model Calibration 71 3.6 Summary 73 4 LIGHTENING ASYNCHRONOUS PIPELINE CONTROLLER 75 4.1 Preliminaries and State-of-the-Art Work 75 4.1.1 Bundled-data vs. Dual-rail Asynchronous Circuits 75 4.1.2 Two-phase vs. Four-phase Bundled-data Protocol 76 4.1.3 Conventional State-of-the-Art Pipeline Controller Template 77 4.2 Delay Path Sharing for Lightening Pipeline Controller Template 78 4.2.1 Synthesizing Sharable Delay Paths 78 4.2.2 Validating Logical Correctness for Sharable Delay Paths 80 4.2.3 Reformulating Timing Constraints of Controller Template 81 4.2.4 Minimally Allocating Delay Buffers 87 4.3 In-depth Pipeline Controller Template Synthesis with Delay Path Reusing 88 4.3.1 Synthesizing Delay Path Units 88 4.3.2 Validating Logical Correctness of Delay Path Units 89 4.3.3 Updating Timing Constraints for Delay Path Units 91 4.3.4 In-depth Synthesis Flow Utilizing Delay Path Units 95 4.4 Experimental Results 99 4.4.1 Environment Setup 99 4.4.2 Piecewise Linear Modeling of Delay Path Unit Area 99 4.4.3 Comparison of Power, Performance, and Area 102 4.5 Summary 107 5 CONCLUSION 109 5.1 Chapter 2 109 5.2 Chapter 3 110 5.3 Chapter 4 110 Abstract (In Korean) 127Docto

Spike neural network architecture with memristive synapses using predictive Cmos model

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2020.O seguinte trabalho tem como objetivo propor e avaliar um circuito eletrônico que implemente uma arquitetura de rede neural com neurônios spike. Transistores do tipo MOSFET foram utilizados para implementar os neurônios. A rede proposta possui aprendizado do tipo spike timing dependent plasticity (STDP). Memristors foram utilizados como sinapses. A validação dos módulos de circuitos e do circuito da arquitetura completa foram realizados utilizando modelos SPICE dos dispositivos. Como a maioria dos dados sobre tecnologias de empresas possuem acesso restrito, algumas universidades fornecem modelos preditivos dos dispositivos com o intuito de reproduzir o comportamento real em tecnologias futuras, que foram empregados neste trabalho. Nesse projeto apresentamos dois tipos de Integrate and Fire Neuron(I&F) usando tecnologia CMOS de 32nm simulada no LTspice empregando o modelo BSIM4v4 concebido pela Universidade de Berkley e aplicando parâmetros preditivos fornecidos pelo Predictive Technology Model (PTM). Os resultados da simulação obtidos aqui, reduzem a tensão da fonte e o tamanho do chip em relação aos designs semelhantes mais recentemente implementado. Além disso, a comunicação entre neurônios e sinapses com um aprendizado STDP foi simulada com êxito.CAPESThe following work aims to establish and evaluate an electronic circuit that implements a neural network architecture with spike neurons. This project uses MOSFET-type transistors to achieve the neurons, and the proposed network has a spike-timing-dependent plasticity (STDP) learning aspect. It applies Memristors to function as synapses. The validation of the circuit modules and the circuit for complete architecture were performed using SPICE models of the devices. Since most data of company technologies is restricted, some universities provide predictive models to reproduce the real ones. In this dissertation, we present two types of Integrate and Fire Neuron (IF) (IF) using 32nm CMOS technology simulated in LTspice with BSIM4v4 model designed by Berkley University and applying predictive parameters provided by Predictive Technology Model (PTM). The simulation results obtained here reduces the font tension and the chip size to the most recent designs implemented. Communication between neurons and synapses with STDP learning has been successfully simulated

Approche industrielle aux boîtes quantiques dans des dispositifs de silicium sur isolant complètement déplété pour applications en information quantique

La mise en oeuvre des qubits de spin électronique à base de boîtes quantiques réalisés en utilisant une technologie avancée de métal-oxyde-semiconducteur complémentaire (en anglais: CMOS ou Complementary Metal-Oxide-Semiconductor) fonctionnant à des températures cryogéniques permet d’envisager la fabrication industrielle reproductible et à haut rendement de systèmes de qubits de spin à grande échelle. Le développement d’une architecture de boîtes quantiques à base de silicium fabriquées en utilisant exclusivement des techniques de fabrication industrielle CMOS constitue une étape majeure dans cette direction. Dans cette thèse, le potentiel de la technologie UTBB (en anglais: Ultra-Thin Body and Buried oxide) silicium sur isolant complétement déplété (en anglais: FD-SOI ou Fully Depleted Silicon-On-Insulator) 28 nm de STMicroelectronics (Crolles, France) a été étudié pour la mise en oeuvre de boîtes quantiques bien définies, capables de réaliser des systèmes de qubit de spin. Dans ce contexte, des mesures d’effet Hall ont été réalisées sur des microstructures FD-SOI à 4.2 K afin de déterminer la qualité du noeud technologique pour les applications de boîtes quantiques. De plus, un flot du processus d’intégration, optimisé pour la mise en oeuvre de dispositifs quantiques utilisant exclusivement des méthodes de fonderie de silicium pour la production de masse est présenté, en se concentrant sur la réduction des risques de fabrication et des délais d’exécution globaux. Enfin, deux géométries différentes de dispositifs à boîtes quantiques FD-SOI de 28nm ont été conçues et leurs performances ont été étudiées à 1.4 K. Dans le cadre d’une collaboration entre Nanoacademic Technologies, Institut quantique et STMicroelectronics, un modèle QTCAD (en anglais: Quantum Technology Computer-Aided Design) en 3D a été développé pour la modélisation de dispositifs à boîtes quantiques FD-SOI. Ainsi, en complément de la caractérisation expérimentale des structures de test via des mesures de transport et de spectroscopie de blocage de Coulomb, leur performance est modélisée et analysée à l’aide du logiciel QTCAD. Les résultats présentés ici démontrent les avantages de la technologie FD-SOI par rapport à d’autres approches pour les applications de calcul quantique, ainsi que les limites identifiées du noeud 28 nm dans ce contexte. Ce travail ouvre la voie à la mise en oeuvre des nouvelles générations de dispositifs à boîtes quantiques FD-SOI basées sur des noeuds technologiques inférieurs.Abstract: Electron spin qubits based on quantum dots implemented using advanced Complementary Metal-Oxide-Semiconductor (CMOS) technology functional at cryogenic temperatures promise to enable reproducible high-yield industrial manufacturing of large-scale spin qubit systems. A milestone in this direction is to develop a silicon-based quantum dot structure fabricated using exclusively CMOS industrial manufacturing techniques. In this thesis, the potential of the industry-standard process 28 nm Ultra-Thin Body and Buried oxide (UTBB) Fully Depleted Silicon-On-Insulator (FD-SOI) technology of STMicroelectronics (Crolles, France) was investigated for the implementation of well-defined quantum dots capable to realize spin qubit systems. In this context, Hall effect measurements were performed on FD-SOI microstructures at 4.2 K to determine the quality of the technology node for quantum dot applications. Moreover, an optimized integration process flow for the implementation of quantum devices, using exclusively mass-production silicon-foundry methods is presented, focusing on reducing manufacturing risks and overall turnaround times. Finally, two different geometries of 28 nm FD-SOI quantum dot devices were conceived, and their performance was studied at 1.4 K. In the framework of a collaboration between Nanoacademic Technologies, Institut quantique, and STMicroelectronics, a 3D Quantum Technology Computer-Aided Design (QTCAD) model was developed for FD-SOI quantum dot device modeling. Therefore, along with the experimental characterization of the test structures via transport and Coulomb blockade spectroscopy measurements, their performance is modeled and analyzed using the QTCAD software. The results reported here demonstrate the advantages of the FD-SOI technology over other approaches for quantum computing applications, as well as the identified limitations of the 28 nm node in this context. This work paves the way for the implementation of the next generations of FD-SOI quantum dot devices based on lower technology nodes

Harnessing noise to enhance robustness vs. efficiency trade-off in machine learning

While deep nets have achieved human-comparable accuracy in various classification tasks, they fall short significantly in terms of the robustness and cost metrics. For example, tiny engineered corruptions in deep net inputs can reduce their accuracy to zero. Furthermore, deep nets also require millions of trainable parameters, resulting in significant training and inference costs. These robustness and cost challenges are well recognized today. In response, there have been a plethora of works focusing on improving either the accuracy vs. robustness trade-off, or the accuracy vs. cost trade-off. However, simultaneous consideration of accuracy, robustness, and cost metrics is largely absent today, in part, because far fewer works have explored the robustness vs. cost trade-off. This dissertation aims to fill this gap by focusing explicitly on the robustness vs. cost trade-off in the presence of data noise, as well as hardware noise. Specifically, we explore how to harness the noise in order to enhance this trade-off. We characterize and improve robustness vs. cost trade-offs across diverse problem settings, ranging from beyond-CMOS hardware implementations of machine learning (ML) classifiers to efficient training of deep nets that are robust to multiple types of corruptions in their inputs. This dissertation can be roughly divided into two part, one focusing on hardware noise and the other on data noise. In the first part, we start by focusing on harnessing noise in spintronic hardware implementations, where the logic gates become error prone when operated at lower switching energy/delay. We propose techniques to shape the resulting hardware noise distribution and to efficiently compensate it at the system-level output. As a result, we observe 1000x improvement intolerance to gate-level switching error rates, while keeping the area/energy overhead of compensation circuits to as low as 15%. These robustness enhancements further enable 3× reduction in iso-throughput energy consumption of a binary ML classifier employed for EEG-based seizure detection. Building on this work, we propose spintronic channel networks, exponential decay of spin current to efficiently realize multi-bit dot product computation. We employ error-prone nanomagnets as efficient stochastic slicers biased by spin currents proportional to the likelihood of the classification decision. We achieve 112x-to-22.5x and 14x-to-2.5x higher energy-efficiency over conventional spin-based and 20 nm CMOS designs, respectively, when realizing 10-to-100-dimensional binary classifiers. Furthermore, we also consider the impact of hardware noise originated from process variations and readout circuits in in-memory computing implementations employing non-volatile resistive crossbar arrays. Based on our analysis, we identify design configurations achieving the highest signal-to-noise ratio (SNR), and further estimate how such robustness trades off with the array energy consumption. In the second part, we switch gears to improve the robustness vs. cost trade-off for deep nets in the presence of data noise. Specifically, we focus on the impact of adversarial perturbations in the deep nets inputs. We propose and validate the hypotheses about orientations of dominant subspaces of adversarial perturbations. We demonstrate how changes in the curvature of decision boundary of the deep nets affects the orientations of the adversarial perturbations. Based on these insights we demonstrate how shaped noise can be introduced as a feature to enhance robustness vs. cost trade-off in deep nets. Specifically, we propose shaped noise augmented processing (SNAP), a method to efficiently train deep nets that are robust to multiple types of adversarial perturbations, simultaneously. SNAP prepends a deep net with a shaped noise augmentation layer whose distribution is learned along with the network parameters using any established robust training framework. Based on extensive comparisons with nine state-of-the-art (SOTA) robust training frameworks, we show that SNAP achieves the best robustness vs. training cost trade-off. In particular, it enables 4x reduction in the training cost compared to the SOTA approach published just this last year. Furthermore, thanks to the computational simplicity of SNAP, it is the first technique of its kind that is scalable to large datasets, such as ImageNet

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma