Standard Cell Routing with Reinforcement Learning and Genetic Algorithm in Advanced Technology Nodes

Abstract Laser voltage probing (LVP) has been extensively used for fault isolation over the last decade; however fault isolation in practice primarily relies on good-to-bad comparisons. In the case of complex logic failures at advanced technology nodes, understanding the components of the measured data can improve accuracy and speed of fault isolation. This work demonstrates the use of second harmonic and thermal effects of LVP to improve fault isolation with specific examples. In the first case, second harmonic frequency is used to identify duty cycle degradation. Monitoring the relative amplitude of the second harmonic helps identify minute deviations in the duty cycle with a scan over a region, as opposed to collecting multiple high resolution waveforms at each node. This can be used to identify timing degradation such as signal slope variation as well. In the second example, identifying abnormal data at the failing device as temperature dependent effect helps refine the fault isolation further.

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Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽

10.3390/sym13030471 ◽

2021 ◽

Vol 13 (3) ◽

pp. 471

Author(s):

Jai Hoon Park ◽

Kang Hoon Lee

Keyword(s):

Genetic Algorithm ◽

Reinforcement Learning ◽

Design Space ◽

Learning Algorithm ◽

Computational Design ◽

Computational Method ◽

Learning Ability ◽

Modular Robots ◽

Control Mechanisms ◽

Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

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Soft error optimization of standard cell circuits based on gate sizing and multi-objective genetic algorithm

Proceedings of the 46th Annual Design Automation Conference on ZZZ - DAC '09 ◽

10.1145/1629911.1630042 ◽

2009 ◽

Cited By ~ 14

Author(s):

Weiguang Sheng ◽

Liyi Xiao ◽

Zhigang Mao

Keyword(s):

Genetic Algorithm ◽

Soft Error ◽

Gate Sizing ◽

Standard Cell ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Error Optimization

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Advanced technology nodes, a foundry perspective

10.1063/1.4766478 ◽

2012 ◽

Author(s):

Jürgen Faul ◽

Jan Hoentschel ◽

Maciej Wiatr ◽

Manfred Horstmann

Keyword(s):

Advanced Technology ◽

Technology Nodes

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Optimization of Multilayer Optical Films with Unsupervised Learning, reinforcement learning and genetic algorithm

Frontiers in Optics / Laser Science ◽

10.1364/fio.2020.jm6a.5 ◽

2020 ◽

Author(s):

Jiang Anqing ◽

Osamu Yoshie

Keyword(s):

Genetic Algorithm ◽

Reinforcement Learning ◽

Unsupervised Learning ◽

Optical Films ◽

Learning Reinforcement

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Collaborative Scheduling of Algorithms for Path Planning of Unmanned Systems

Current Chinese Science ◽

10.2174/2210298101666210211094253 ◽

2021 ◽

Vol 01 ◽

Author(s):

Ying Li ◽

Chubing Guo ◽

Jianshe Wu ◽

Xin Zhang ◽

Jian Gao ◽

...

Keyword(s):

Genetic Algorithm ◽

Reinforcement Learning ◽

Path Planning ◽

Unmanned Systems ◽

Ant Colony Optimization Algorithm ◽

A Algorithm ◽

Collaborative Scheduling ◽

Simulation Results ◽

Planning Problems ◽

Effective Path

Background: Unmanned systems have been widely used in multiple fields. Many algorithms have been proposed to solve path planning problems. Each algorithm has its advantages and defects and cannot adapt to all kinds of requirements. An appropriate path planning method is needed for various applications. Objective: To select an appropriate algorithm fastly in a given application. This could be helpful for improving the efficiency of path planning for Unmanned systems. Methods: This paper proposes to represent and quantify the features of algorithms based on the physical indicators of results. At the same time, an algorithmic collaborative scheme is developed to search the appropriate algorithm according to the requirement of the application. As an illustration of the scheme, four algorithms, including the A-star (A*) algorithm, reinforcement learning, genetic algorithm, and ant colony optimization algorithm, are implemented in the representation of their features. Results: In different simulations, the algorithmic collaborative scheme can select an appropriate algorithm in a given application based on the representation of algorithms. And the algorithm could plan a feasible and effective path. Conclusion: An algorithmic collaborative scheme is proposed, which is based on the representation of algorithms and requirement of the application. The simulation results prove the feasibility of the scheme and the representation of algorithms.

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