Long-Term Reliability of Nanometer VLSI Systems

Tan, Sheldon X.-D.; Tahoori, Mehdi; Kim, Tae‐Young; Wang, Shengcheng; Sun, Zeyu; Kiamehr, Saman

doi:10.1007/978-3-030-26172-6

Cited by 17 publications

(7 citation statements)

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“…The notations t ILD , k ILD are the thickness and the conductivity of Inter Layer Dielectric (ILD), which separates the metal wire and underlying layer. The temperature on the multi-segment interconnect tree is assumed to be a combination of the nonuniform distributed temperatures on each segment calculated by (5). For the node connecting to more than one segments, we suppose that there is negligible heat loss between the node and the underlying layer [35].…”

Section: B Space-time Related Temperature Modelmentioning

confidence: 99%

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A Space-Time Neural Network for Analysis of Stress Evolution under DC Current Stressing

Hou¹,

Wong²,

Chen³

et al. 2022

Preprint

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The electromigration (EM)-induced reliability issues in very large scale integration (VLSI) circuits have attracted increased attention due to the continuous technology scaling. Traditional EM models often lead to overly pessimistic prediction incompatible with the shrinking design margin in future technology nodes. Motivated by the latest success of neural networks in solving differential equations in physical problems, we propose a novel mesh-free model to compute EM-induced stress evolution in VLSI circuits. The model utilizes a specifically crafted spacetime physics-informed neural network (STPINN) as the solver for EM analysis. By coupling the physics-based EM analysis with dynamic temperature incorporating Joule heating and via effect, we can observe stress evolution along multi-segment interconnect trees under constant, time-dependent and space-time-dependent temperature during the void nucleation phase. The proposed STPINN method obviates the time discretization and meshing required in conventional numerical stress evolution analysis and offers significant computational savings. Numerical comparison with competing schemes demonstrates a 2× ∼ 52× speedup with a satisfactory accuracy.

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Section: B Space-time Related Temperature Modelmentioning

confidence: 99%

“…For the node connecting to more than one segments, we suppose that there is negligible heat loss between the node and the underlying layer [35]. Based on (5), the temperature of the node over time can be expressed as:…”

Section: B Space-time Related Temperature Modelmentioning

confidence: 99%

A Space-Time Neural Network for Analysis of Stress Evolution under DC Current Stressing

Hou¹,

Wong²,

Chen³

et al. 2022

Preprint

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“…where A HCI is a technology-dependent constant, f is the clock frequency, SW is the switching activity factor, t ox is the oxide thickness, V th is the threshold voltage and V GS is the gatesource voltage of the transistor, E 1 is a constant equal to 0.8 V/ nm [32] and t is the total time. The effect of HCI depends on the temperature of the device.…”

Section: Hot Carrier Injectionmentioning

confidence: 99%

“…Due to the interface traps in the gate oxide, the device characteristics such as threshold voltage and drain current are degraded. HCI [32] effect occurs in NMOS transistors. The effect of HCI in PMOS transistors is negligible.…”

Section: Hot Carrier Injectionmentioning

confidence: 99%

Recycled integrated circuit detection using reliability analysis and machine learning algorithms

Krishnan

Palanisamy

2020

IET Computers & Digital Tech

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The use of counterfeit integrated circuits (ICs) in electronic products decreases its quality and lifetime. Recycled ICs can be detected by the method of aging analysis. Aging is carried out through reliability analysis with the effect of hot carrier injection and bias temperature instability (BTI). In this work, three machine learning methods, namely Kmeans clustering, back propagation neural network (BPNN) and support vector machines (SVMs), are used to detect the recycled IC aged for a shorter period (1 day) with minimum data size. This work also distinguishes the effects of degradation due to process variations and reliability effects. The reliability and Monte Carlo simulation are performed on benchmark circuits such as c17, s27, b02 and fully differential folded-cascode amplifier using the Cadence Virtuoso tool, and the parameters such as minimum voltage, delay value, supply current, gain, phase margin and bandwidth are measured. Machine learning methods are developed using MATLAB to train and classify the parameters. From the results obtained, it is observed that the classification rate for the benchmark circuits is 100%, and using BPNN, K-means clustering and SVM and the proposed method, recycled IC or used IC is detected even if it was used for 1 day.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…11 It is reported that the data center components outage can cost the providers between $8000 and $16,000 per minute. 12 According to the results of the report conducted by Ponemon Institute, the average cost of a data center outage has steadily increased from $505,502 in 2010 to $740,357 in 2016, indicating a 38% increase. 13 According to a report by Information Technology Intelligence Consulting in 2018, an hour of downtime on average costs data center operators $260,000, while a 5-min outage costs $2600.…”

mentioning

confidence: 99%

Availability modeling in redundant OpenStack private clouds

et al. 2021

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In cloud computing services, high availability is one of the quality of service requirements which is necessary to maintain customer confidence. High availability systems can be built by applying redundant nodes and multiple clusters in order to cope with software and hardware failures. Due to cloud computing complexity, dependability analysis of the cloud may require combining state-based and nonstate-based modeling techniques. This article proposes a hierarchical model combining reliability block diagrams and continuous time Markov chains to evaluate the availability of OpenStack private clouds, by considering different scenarios. The steady-state availability, downtime, and cost are used as measures to compare different scenarios studied in the article. The heterogeneous workloads are considered in the proposed models by varying the number of CPUs requested by each customer. Both hardware and software failure rates of OpenStack components used in the model are collected via setting up a real OpenStack environment applying redundancy techniques. Results obtained from the proposed models emphasize the positive impact of redundancy on availability and downtime. Considering the tradeoff between availability and cost, system providers can choose an appropriate scenario for a specific purpose. K E Y W O R D S availability modeling, cloud computing, continuous time Markov chains, hierarchical models, reliability block diagrams INTRODUCTIONCloud computing includes different resources of one or more data centers, such as storage, network and compute, shared through virtualization technology. 1 Infrastructure as a service (IaaS) is an important service model of the cloud computing paradigm in which providers allocate virtual computing resources as virtual machines (VMs) on physical machines (PMs) to customers over the internet. [2][3][4] According to Gartner, 5 the market for public cloud will reach over $306.9 billion at the end of 2021 and $364 billion at the end of 2022. Gartner expects the hyperscale providers, such as Amazon AWS, Google Cloud Platform, Alibaba, and Microsoft Azure, to account for 70% of the market by the end of 2021. 6 Large organization and government agencies usually like to develop their own cloud data centers for satisfying their needs. By contrast, small organizations usually rent cloud resources from cloud providers to solve their own problems. 7

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Long-Term Reliability of Nanometer VLSI Systems

Cited by 17 publications

References 0 publications

A Space-Time Neural Network for Analysis of Stress Evolution under DC Current Stressing

A Space-Time Neural Network for Analysis of Stress Evolution under DC Current Stressing

Recycled integrated circuit detection using reliability analysis and machine learning algorithms

Availability modeling in redundant OpenStack private clouds

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