Carbon nanotube field-effect transistors (CNFETs) are promising candidates for building energy-efficient digital systems at highly scaled technology nodes. However, carbon nanotubes (CNTs) are inherently subject to variations that reduce circuit yield, increase susceptibility to noise, and severely degrade their anticipated energy and speed benefits. Joint exploration and optimization of CNT processing options and CNFET circuit design are required to overcome this outstanding challenge. Unfortunately, existing approaches for such exploration and optimization are computationally expensive, and mostly rely on trial-and-error-based ad hoc techniques. In this paper, we present a framework that quickly evaluates the impact of CNT variations on circuit delay and noise margin, and systematically explores the large space of CNT processing options to derive optimized CNT processing and CNFET circuit design guidelines. We demonstrate that our framework: 1) runs over 100× faster than existing approaches and 2) accurately identifies the most important CNT processing parameters, together with CNFET circuit design parameters (e.g., for CNFET sizing and standard cell layouts), to minimize the impact of CNT variations on CNFET circuit speed with ≤5% energy cost, while simultaneously meeting circuit-level noise margin and yield constraints.

The quest for technologies with superior device characteristics has showcased Carbon-Nanotube Field-Effect Transistors (CNFET) into limelight. In this work we present physical design techniques to improve the yield of CNFET circuits in the presence of Carbon Nanotube (CNT) imperfections. Various layout schemes are studied for enhancing the yield of CNFET standard cell library. With the help of existing ASIC design flow, we perform system-level benchmarking of CNFET circuits and compare them to CMOS circuits at various technology nodes. With CNFET technology, we observe maximum performance gains for circuits with gate-dominated delays. Averaged across various benchmarks at 16 nm, we report 8× improvement in Energy-Delay-Product (EDP) with CNFET circuits when compared to CMOS counterpart. We also study the performance of a complete OpenRISC processor, where we see 1.5× improvement in EDP over CMOS at 16 nm technology node. Voltage scaling enabled by CNFETs can be explored in the future for further performance benefits.

Carbon nanotube field-effect transistors (CNFETs) are promising candidates for building energy-efficient digital systems at highly-scaled technology nodes. However, carbon nanotubes (CNTs) are inherently subject to variations that reduce circuit yield, increase susceptibility to noise, and severely degrade their anticipated energy and speed benefits. Joint exploration and optimization of CNT processing options and CNFET circuit design are required to overcome this outstanding challenge. Unfortunately, existing approaches for such exploration and optimization are computationally expensive, and mostly rely on trial-and-error-based ad-hoc techniques. In this paper, we present a systematic framework which quickly evaluates the impact of CNT variations on circuit delay and noise margin, and automatically explores the large space of CNT processing options to derive optimized CNT processing and CNFET circuit design guidelines. We demonstrate that: 1. Our new framework runs over 100X faster than existing approaches. 2. It accurately identifies the most important CNT processing parameters, together with CNFET circuit sizing, to minimize the impact of CNT variations while meeting circuit-level noise margin constraints.

Carbon Nanotube Field-Effect Transistors (CNFETs) are excellent candidates for building highly energy-efficient digital systems. However, imperfections inherent in carbon nanotubes (CNTs) pose significant hurdles to realizing practical CNFET circuits. In order to achieve CNFET VLSI systems in the presence of these inherent imperfections, careful orchestration of design and processing is required: from device processing and circuit integration, all the way to large-scale system design and optimization. In this paper, we summarize the key ideas that enabled the first experimental demonstration of CNFET arithmetic and storage elements. We also present an overview of a probabilistic framework to analyze the impact of various CNFET circuit design techniques and CNT processing options on system-level energy and delay metrics. We demonstrate how this framework can be used to improve the energy-delay-product (EDP) of CNFET-based digital systems.

Carbon nanotube field-effect transistors (CNFETs) are excellent candidates for building highly energy-efficient electronic systems of the future. Fundamental limitations inherent to carbon nanotubes (CNTs) pose major obstacles to the realization of robust CNFET digital very large-scale integration (VLSI): 1) it is nearly impossible to guarantee perfect alignment and positioning of all CNTs despite near-perfect CNT alignment achieved in recent years; 2) CNTs can be metallic or semiconducting depending on chirality; and 3) CNFET circuits can suffer from large performance variations, reduced yield, and increased susceptibility to noise. Today's CNT process improvements alone are inadequate to overcome these challenges. This paper presents an overview of: 1) imperfections and variations inherent to CNTs; 2) design and processing techniques, together with a probabilistic analysis framework, for robust CNFET digital VLSI circuits immune to inherent CNT imperfections and variations; and 3) recent experimental demonstration of CNFET digital circuits that are immune to CNT imperfections. Significant advances in design tools can enable robust and scalable CNFET circuits that overcome the challenges of the CNFET technology while retaining its energy-efficiency benefits.

Carbon Nanotube Field-Effect Transistors (CNFETs) are excellent candidates for designing highly energy-efficient future digital systems. However, carbon nanotubes (CNTs) are inherently highly subject to imperfections that pose major obstacles to robust CNFET digital VLSI. This paper summarizes commonly raised questions and concerns about CNFET technology through a series of frequently asked questions. The specific questions addressed in this paper are motivated by recent advances in the field since the publication of our earlier paper on frequently asked questions in the Proceedings of the 2009 Design Automation Conference.

Variations in the spatial density of carbon nanotubes (CNTs), resulting from the lack of precise control over CNT positioning during chemical synthesis, is a major hurdle to the scalability of carbon nanotube field effect transistor (CNFET) circuits. Such CNT density variations can lead to non-functional CNFET circuits. This paper presents a probabilistic framework for modeling the CNT count distribution contained in a CNFET of given width, and establishes the accuracy of the model using experimental data obtained from CNT growth. Using this model, we estimate the impact of CNT density variations on the yield of CNFET very large-scale integrated circuits. Our estimation results demonstrate that CNT density variations can significantly degrade the yield of CNFETs, and can be a major concern for scaled CNFET circuits. Finally, we analyze the impact of CNT correlation (i.e., correlation of CNT count between CNFETs) that exists in CNT growth, and demonstrate how the yield of a CNFET storage circuit (primarily limited by its noise immunity) can be significantly improved by taking advantage of such correlation.

Carbon Nanotubes (CNTs) are grown using chemical synthesis, and the exact positioning and chirality of CNTs are very difficult to control. As a result, "small-width" Carbon Nanotube Field-Effect Transistors (CNFETs) can have a high probability of containing no semiconducting CNTs, resulting in CNFET failures. Upsizing these vulnerable small-width CNFETs is an expensive design choice since it can result in substantial area/power penalties. This paper introduces a processing/design co-optimization approach to reduce probability of CNFET failures at the chip-level. Large degree of spatial correlation observed in directional CNT growth presents a unique opportunity for such optimization. Maximum benefits from such correlation can be realized by enforcing the active regions of CNFETs to be aligned with each other. This approach relaxes the device-level failure probability requirement by 350X at the 45nm technology node, leading to significantly reduced costs associated with upsizing the small-width CNFETs.

Carbon Nanotube Field-Effect Transistors (CNFETs) can potentially provide significant energy-delay-product benefits compared to silicon CMOS. However, CNFET circuits are subject to several sources of imperfections. These imperfections lead to incorrect logic functionality and substantial circuit performance variations. Processing techniques alone are inadequate to overcome the challenges resulting from these imperfections. An imperfection-immune design methodology is required. We present an overview of imperfection-immune design techniques to overcome two major sources of CNFET imperfections: metallic Carbon Nanotubes (CNTs) and CNT density variations.

Carbon Nanotube Field-Effect Transistors (CNFETs) show promise as extensions to silicon-CMOS. Ideal CNFET circuits can potentially provide 20X Energy-Delay-Product benefits over silicon-CMOS at the 16 nm technology node. However, several challenges must be overcome before such performance benefits can be experimentally realized. In this paper, we present a brief overview of CNFET technology, and address commonly raised concerns through a series of Frequently Asked Questions (FAQs). We also provide a CNFET technology outlook which includes a survey of challenges as well as existing and potential solutions to these challenges.

Carbon Nanotubes (CNTs) are grown using chemical synthesis. As a result, it is extremely difficult to ensure exact positioning and uniform density of CNTs. Density variations in CNT growth can compromise reliability of Carbon Nanotube Field Effect Transistor (CNFET) circuits, and result in increased delay variations. A parameterized model for CNT density variations is presented based on experimental data extracted from aligned CNT growth. This model is used to quantify the impact of such variations on design metrics such as noise margin and delay variations of CNFET circuits. Finally, we analyze correlation that exists in aligned CNT growth, and demonstrate how the reliability of CNFET circuits can be significantly improved by taking advantage of such correlation.

The quest for technologies with superior device characteristics has showcased Carbon Nanotube Field Effect Transistors (CNFETs) into limelight. Among the several design aspects necessary for today's grail in CNFET technology, achieving functional immunity to Carbon Nanotube (CNT) manufacturing issues (such as mispositioned CNTs and metallic CNTs) is of paramount importance. In this work we present a new design technique to build compact layouts while ensuring 100% functional immunity to mispositioned CNTs. Then, as second contribution of this work, we have developed a CNFET Design Kit (DK) to realize a complete design flow from logic-to-GDSII traversing the conventional CMOS design flow. This flow enables a framework that allows accurate comparison between CMOS and CNFET-based circuits. This paper also presents simulation results to illustrate such analysis, namely, a CNFET-based inverter can achieve gains, with respect to the Energy-Delay Product (EDP) metric, of more than 4× in delay, 2× in energy/cycle and significant area savings (more than 30%) when compared to a corresponding CMOS inverter benchmarked with an industrial 65nm technology.

Carbon Nanotube Field-Effect Transistors (CNFETs) show big promise as extensions to silicon-CMOS because: 1) Ideal CNFETs can provide significant energy and performance benefits over silicon-CMOS, and 2) CNFET processing is compatible with existing silicon-CMOS processing. However, future gigascale systems cannot rely solely on existing chemical synthesis for guaranteed ideal devices. VLSI-scale logic circuits using CNFETs must overcome major challenges posed by: 1) Misaligned and mis-positioned Carbon Nanotubes (CNTs); 2) Metallic CNTs; and, 3) CNT density variations. This paper performs detailed analysis of the impact of these challenges on CNFET circuit performance. A combination of design and processing techniques, presented this paper, can enable VLSI-scale CNFET logic circuits that are immune to high rates of inherent imperfections. These techniques are inexpensive compared to traditional defect- and fault-tolerance, do not impose major changes in VLSI design flows, and are compatible with VLSI processing because they do not require special customization on chip-by-chip basis.

Metallic carbon nanotubes (CNTs) pose a major barrier to the design of digital logic circuits using CNT field-effect transistors (CNFETs). Metallic CNTs create source to drain shorts in CNFETs, resulting in undesirable effects such as excessive leakage and degraded noise margins. No known CNT growth technique guarantees 0% metallic CNTs. Therefore, special processing techniques are required for removing metallic CNTs after CNT growth. This paper presents a probabilistic model which incorporates processing and design parameters and enables quantitative analysis of the impact of metallic CNTs on leakage, noise margin, and delay variations of CNFET-based digital logic circuits. With practical constraints on these key circuit performance metrics, the model provides design and processing guidelines that are required for very large scale integration (VLSI)-scale metallic-CNT-tolerant digital circuits.

Metallic carbon nanotubes (CNTs) create source-drain shorts in carbon nanotube field effect transistors (CNFETs), causing excessive leakage, degraded noise margin and delay variation. There is no known CNT growth technique that guarantees 0% metallic CNTs. Therefore, metallic CNT removal techniques are necessary. Unfortunately, such removal techniques alone are imperfect and insufficient. This paper demonstrates the necessity for co-optimization of processing techniques for metallic CNT removal together with CNFET-based circuit design. We present a probabilistic CNFET circuit model which forms the basis for such co-optimization, and use the model to derive design and processing guidelines that enable design of CNFET-based digital circuits with practical constraints on leakage, noise margin and delay variations. These guidelines are essential for designing robust metallic- carbon-nanotube-tolerant digital circuits.