CAD for Analog/Mixed‐Signal Integrated Circuits

Budak, Ahmet F.; Pan, David Z.; Chen, Hao; Zhu, Keren; Liu, Mingjie; Alawieh, Mohamed Baker; Zhang, Shuhan; Shi, Wei; Tang, Xiyuan

doi:10.1002/9781119869610.ch3

Cited by 3 publications

(1 citation statement)

References 96 publications

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“…In order to achieve this challenge designers, need to apply several CAD tool design methodologies [22]. IC optimization techniques and novel CAD tools are applied in parallel for the realization of efficient ICs and for reducing the resource consumption [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Special Issue “Smart IC Design and Sensing Technologies”

Floros

Tziouvaras

2022

Chips

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Smart sensing technologies and their inherent data-processing techniques have drawn considerable research and industrial attention in recent years. Recent developments in nanometer CMOS technologies have shown great potential to deal with the increasing demand of processing power that arises in these sensing technologies, from IoT applications to complicated medical devices. Moreover, circuit implementation, which could be based on a full analog or digital approach or, in most cases, on a mixed-signal approach, possesses a fundamental role in exploiting the full capabilities of sensing technologies. In addition, all circuit design methodologies include the optimization of several performance metrics, such as low power, low cost, small area, and high throughput, which impose critical challenges in the field of sensor design. This Special Issue aims to highlight advances in the development, modeling, simulation, and implementation of integrated circuits for sensing technologies, from the component level to complete sensing systems.

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Section: Introductionmentioning

confidence: 99%

Special Issue “Smart IC Design and Sensing Technologies”

Floros

Tziouvaras

2022

Chips

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Analysis of Mixed Signal Low Power and Semiconductor Devices Applications Electronics and Computer system

Bhat

2023

2023 2nd International Conference on Edge Computing and Applications (ICECAA)

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AnGeL: Fully-Automated Analog Circuit Generator Using a Neural Network Assisted Semi-Supervised Learning Approach

Fayazi

Taba

Afshari

et al. 2023

IEEE Trans. Circuits Syst. I

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Automatic synthesis of analog and Radio Frequency (RF) circuits is a trending approach that requires an efficient circuit modeling method. This is due to the expensive cost of running a large number of simulations at each synthesis cycle. Artificial intelligence methods are promising approaches for circuit modeling due to their speed and relative accuracy. However, existing approaches require a large amount of training data, which is still collected using simulation runs. In addition, such approaches collect a whole separate dataset for each circuit topology even if a single element is added or removed. These matters are only exacerbated by the need for post-layout modeling simulations, which take even longer. To alleviate these drawbacks, in this paper, we present FuNToM, a functional modeling method for RF circuits. FuNToM leverages the two-port analysis method for modeling multiple topologies using a single main dataset and multiple small datasets. It also leverages neural networks which have shown promising results in predicting the behavior of circuits. Our results show that for multiple RF circuits, in comparison to the state-of-the-art works, while maintaining the same accuracy, the required training data is reduced by 2.8x -10.9x. In addition, FuNToM needs 176.8x -188.6x less time for collecting the training set in post-layout modeling.

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CAD for Analog/Mixed‐Signal Integrated Circuits

Cited by 3 publications

References 96 publications

Special Issue “Smart IC Design and Sensing Technologies”

Special Issue “Smart IC Design and Sensing Technologies”

Analysis of Mixed Signal Low Power and Semiconductor Devices Applications Electronics and Computer system

AnGeL: Fully-Automated Analog Circuit Generator Using a Neural Network Assisted Semi-Supervised Learning Approach

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