Design Automation Research Articles

A comparative analysis of traditional and AI-based routing algorithms in electronic design automation

Rapid advances in artificial intelligence (AI) and machine learning have had a significant impact on various fields, including electronic design automation (EDA). This study aims to compare current EDA routing algorithms with AI-based routing algorithms, highlighting their respective advantages and limitations. Through a comprehensive analysis of existing EDA routing algorithms and artificial intelligence-based technologies, this study explores the applicability of these algorithms in different EDA scenarios, with a focus on their effectiveness and efficiency in routing tasks, and aims to compare and contrast traditional and AI-based routing algorithms in the context of electronic design automation. It also shows that the current AI technology will play an important role in the future development of EDA software. Therefore, the combination of AI technology may be the focus of EDA software development, so as to help elucidate the evolving landscape of EDA and provide insights into the potential future direction, and provide a a comprehensive understanding of their underlying methodologies, advantages, and limitations of traditional and AI-based algorithms in the context of electronic design automation.

An automatic design-feedback process for structural prefabricated components quantity take-off calculation using BIM

ABSTRACT Prefabricated construction is spreading widely for its various advantages like construction efficiency, quality assurance, and environmental protection. However, ensuring project cost-effectiveness under the ever-increasing demands of prefabrication rate requirements has become a challenge. There is a growing need for Quantity Take-off (QTO) feedback on building components for stakeholders in the initial stages of structural design. This paper aims to create an automated design-feedback workflow for the structural QTO using the BIM-assisted visual programming tool Dynamo. The workflow includes model construction, data analysis, unit price data encoding, and design automation. The proposed method demonstrates the pattern of data exchange between stakeholders and is automated by the created visual programming scripts, breaking the original technical limitations, and presenting timely QTO feedback to the clients in a straightforward format. This study contributes to the field of design communication while satisfying the client’s requirements at the structural design stage.

Shallow Clock Tree Pre-Estimation for Designing Clock Tree Synthesizable Verilog RTLs

Clock tree synthesis (CTS) is an important process in determining overall chip timing and power consumption. The CTS is also a time-consuming process for checking the clock tree. If the chip design and specification do not match, the CTS result will be wrong. Many users use licensed electronic design automation (EDA) tools like Synopsys, and Cadence to carry out accurate chip verification. However, when using a licensed EDA tool, it is difficult to change the function and confirm the overall process in detail. If the design is wrong, the expected cost is doubled, as it will be necessary to modify the design and check all processes for verification. Currently, it cannot check the synthesizability of the clock tree on the placement and route process using only RTL. The main purpose of this study is to predict the CTS result of pre-estimation roughly using an RTL source placing temporary logics using random buffer insertion before the route process: then the incorrectly designed part can be freely modified because the CTS result can be known in advance. Experimental results showed that this research achieves an increase in inserted buffer area by about 10%, the standard deviation of clock skew achieves zero clock skew after shallow CTS, and clock frequency increases by about 10%. This paper contributes to optimizing clock tree implementation by conducting the pre-route process before using the CTS tool. Also, our approach not only minimizes resource usage but also optimizes CTS for the RTL structure. It holds considerable value in enhancing the efficiency and performance of integrated circuits.

A Toolbox for Designing 3D‐Printing‐Ready Pneumatic Circuits for Controlling Soft Robots

The robotics field has witnessed a rapid increase in the popularity of soft pneumatic robots due to their adaptability to unstructured environments and safe human–robot interaction. However, many of them are still controlled by rigid solenoid valves and microcontrollers. Recent research works have introduced various electronics‐free computational devices for soft robotic applications. Yet, connecting these individual components into pneumatic circuits still remains challenging. The manual circuit design, fabrication, and piping process can bring inefficient circuit layout, redundant tubing, unreliable fittings, and potentially human mistakes. This work presents an open‐source toolbox for automatically designing pneumatic circuits for soft robotic applications. The toolbox takes the desired computation function along with some space constraints as inputs, and generates a 3D‐printable file of a pneumatic circuit that can be fabricated and used directly. This is achieved by efficiently positioning the logic gates and air channels while considering the workspace constraints, fabrication limits, material cost, and system complexity. The versatility of the design automation algorithm has been tested with two different truth tables and six different workspaces. The work also showcases utilizing the toolbox to design a computational circuit that governs the movements of a soft robotic hand with four pneumatic actuators.

Factoring Interacting Stress Mechanisms in Design for Reliability of Extreme Environment Power Modules

As power densification demands are placing electronic packages under greater reliability risk, the consequence of complementary or interacting stresses in producing failure are becoming increasingly significant. As such, it is important that reliability methods and package designs consider how multiple-stress interactions may impact product life. Here, the coordination between a novel accelerated testing method and electronic design automation efforts has demonstrated a successful optimization approach for a wire-bonded 2D module layout combining failure mechanisms of electromigration and mechanical stressing. Utilizing custom, physics-of-failure approaches in accelerated testing, interactions can be observed in failure acceleration, which then can be incorporated into design for reliability (DfR) optimization tools. The PowerSynth 2 platform has been utilized as a design for reliability tool to perform a rapid relative reliability evaluation incorporating multi-stress scenarios. This work demonstrates the value added to reliability evaluation techniques when accounting for interacting failure mechanisms and suggests that next generation power devices consider these effects in lifetime estimation.

A prospective analysis of the engineering design discipline evolution based on key influencing trends

Design and manufacturing sectors are vital agents of an economy. However, multiple challenges influence product designs such as the predicted scarcity of energy and primary materials, the ubiquitous integration of electronic components and computing science in systems’ architectures, the pervasive production of data by most systems, the emphasis given to CO2 free energy solutions, recycling, and reuse, the transformation of the consumption model from product ownership to product as a service, as well as the geopolitical conflicts. Major technological advancements leading to transformation in socio-economic practices would be required to address these challenges which can have a profound effect on design and manufacturing activities. This research aims to evaluate the potential impact and modification induced by such transformations on product design process. The research identifies that early design automation can enable coping with unmanageable cognitive load generated by cascading changes. A list of modifications to current design practices is proposed to enable the development of a new generation of design tools. The article provides an initial prospective effort to discuss the potential services and functionality that will be offered by future design tools’.

Multidisciplinary Design Automation of Electric Motors—Systematic Literature Review and Methodological Framework

Electric motor development is a challenging task, as higher efficiency requirements and various interdependencies between different engineering domains must be considered. Established design approaches often lack the ability to address these interdependencies because they focus on specific domains and properties. Automated, multidisciplinary design approaches hold untapped potential for optimizing motors in terms of diverse requirements and advancing the development of more efficient and reliable motors. This paper presents a systematic literature review of the current state of research in the multidisciplinary design automation of electric motors. The literature basis comprises 1005 publications that are identified by a systematic internet search. The review of the existing approaches is based on twelve criteria that characterize the design automation task in general, such as knowledge representation or reasoning methods used, as well as criteria specific to electric motor design, such as domains considered and their coupling. The analysis reveals what current approaches are lacking: Consequent analysis and integration of domains, applicability of suggested methods, incorporation of established multidisciplinary design optimization (MDO) architectures, alongside the consideration of passive components in the motor. Aside from the introduction of twelve criteria for systematic charaterization of multidisciplinary design automation of electric motors, this article expands the state of the art by proposing an initial framework to establish process chains tackling the identified gaps in the review.

Structural optical design of multi-group, high zoom ratio optical systems

In this paper, we propose a method for the structural optical design of multi-group, high zoom ratio optical systems based on matrix optics and Particle Swarm Optimization (PSO) algorithm. This method effectively addresses the problems of initial structural calculation difficulties, large volume, and unsmooth zoom curves with large inflection points in high zoom ratio optical systems. To design the zoom optical system, the matrix equations of the multi-group zoom system are derived using geometric optics; and a mathematical model is built to solve the initial structural parameters of the zoom system based on the matrix. Then, a new fitness function is introduced as an indicator to iteratively optimize the initial structural parameters of the system in multi-dimensional space, considering factors such as maximum zoom ratio, total optical length, and full field of view angle. Finally, this method is validated in two complex optical system designs including a 20x four-group and a 35x five-group zoom optical system, showing strong retrieval capabilities. Independent of traditional trial-and-error investigations for solutions, our method facilitates the automatic intelligent design of complex optical systems.

On the automatic design of multi-objective particle swarm optimizers: experimentation and analysis

Research in multi-objective particle swarm optimizers (MOPSOs) progresses by proposing one new MOPSO at a time. In spite of the commonalities among different MOPSOs, it is often unclear which algorithmic components are crucial for explaining the performance of a particular MOPSO design. Moreover, it is expected that different designs may perform best on different problem families and identifying a best overall MOPSO is a challenging task. We tackle this challenge here by: (1) proposing AutoMOPSO, a flexible algorithmic template for designing MOPSOs with a design space that can instantiate thousands of potential MOPSOs; and (2) searching for good-performing MOPSO designs given a family of training problems by means of an automatic configuration tool (irace). We apply this automatic design methodology to generate a MOPSO that significantly outperforms two state-of-the-art MOPSOs on four well-known bi-objective problem families. We also identify the key design choices and parameters of the winning MOPSO by means of ablation. AutoMOPSO is publicly available as part of the jMetal framework.

Landauer-QFLPS Model for Mixed Schottky-Ohmic Contact Two-Dimensional Transistors.

Two-dimensional material-based field-effect transistors (2DM-FETs) are playing a revolutionary role in electronic devices. However, before electronic design automation (EDA) for 2DM-FETs can be achieved, it remains necessary to determine how to incorporate contact transports into model. Reported methods compromise between physical intelligibility and model compactness due to the heterojunction nature. To address this, quasi-Fermi-level phase space theory (QFLPS) is generalized to incorporate contact transports using the Landauer formula. It turns out that the Landauer-QFLPS model effectively overcomes the issue of concern. The proposed new formula can describe 2DM-FETs with Schottky or Ohmic contacts with superior accuracy and efficiency over previous methods, especially when describing non-monotonic drain conductance characteristics. A three-bit threshold inverter quantizer (TIQ) circuit is fabricated using ambipolar black phosphorus and it is demonstrated that the model accurately predicts circuit performance. The model could be very effective and valuable in the development of 2DM-FET-based integrated circuits.

Identifying Emerging Technologies and Skills Required for Construction 4.0

Connectivity, digitization, and emerging Industry 4.0 technology such as AI, automation, robotics, IoT, and cyber-physical space have transformed social relations, the means of production, and the world of labor. The construction industry has also been transformed by Industry 4.0 technologies, mainly through Building Information Modeling (BIM)-based digitization. This revolution in the construction industry has been called Construction 4.0 and is based on the use of innovative technologies that promote digitalization and automation of design, construction, and management processes. Furthermore, the workforce for the construction industry in the context of Construction 4.0 must have adequate skills for this new scenario. Previous literature reviews have analyzed the idea of transitioning the construction industry to Construction 4.0 and implementing specific technologies in the industry. However, there has been limited exploration of the skills necessary for the Construction 4.0 workforce. This article aims to address this gap by identifying the technologies and skills that have been scientifically researched and applied in the construction industry, specifically related to the concepts of Industry 4.0 and Construction 4.0. Utilizing a scoping literature review in the Scopus database, this study seeks to identify: (i) emergent C4.0 technologies in the AEC industry; and (ii) which skills or competencies are demanded when using these technologies. All of our bibliographical searches are inserted and restricted to the concept of Construction 4.0. A total of 108 articles related to technologies and 15 related to skills in the Construction 4.0 context were selected for analysis. Twenty-one technologies were identified and grouped into five sets according to their similarities and applications: AI-assisted technologies, Advanced manufacture, Smart tools, Digital simulation/Visualization, and Data acquisition/detection. The skills were identified and grouped into soft and hard skills. However, the findings point out that the new skills needed by the workforce are described in a dispersed way, without a central theme of research in the context of Construction 4.0. Our findings contribute to a better understanding of the transformations towards Construction 4.0 and provide data for industry and universities to respond appropriately to the new demands of the construction industry.

Inverse Design of Two-Dimensional Shape-Morphing Structures

Abstract This study proposes an inverse method for synthesizing shape-morphing structures in the lateral direction by integrating two-dimensional hexagonal unit cell with curved beams. Analytical expressions are derived to formulate the effective Young’s modulus and Poisson’s ratio for the base unit-cell as a function of its geometric parameters. The effective lateral Poisson’s ratio can be controlled by manipulating a set of geometric parameters, resulting in a dataset of over 6000 data points with Poisson’s ratio values ranging from −1.2 to 10.4. Furthermore, we utilize the established dataset to train an inverse design framework that utilizes a physics-guided neural network algorithm, and the framework can predict design parameters for a targeted shape-morphing structure. The proposed approach enables the generation of structures with tailored Poisson’s ratio ranging from −1.2 to 3.4 while ensuring flexibility and reduced stress concentration within the predicted structure. The generated shape-morphing structures’ performance is validated through numerical simulation and physical tensile testing. The finite element analysis (FEA) simulation results confirm agreement with the designed values for the shape-morphing structure, and the tensile testing results reveal the same trend in shape-morphing behavior. The proposed design automation framework demonstrates the feasibility of creating intricate and practical shape-morphing structures with high accuracy and computational efficiency.

A Study of Sustainable Product Design Evaluation Based on the Analytic Hierarchy Process and Deep Residual Networks

Traditional product design evaluation processes are resource-intensive and time-consuming, resulting in unsustainably higher costs and longer lead times. Therefore, sustainable product design evaluation has become an increasingly crucial aspect of product design, focusing on creating a high-efficiency, high-reliability, and low-carbon-emission approach. In this study, we proposed an integrated approach that combines manual design evaluation based on the analytic hierarchy process (AHP) with an automatic design evaluation based on a ResNet-50 network in order to develop a sustainable design evaluation method. First, the evaluation level and indicators for the shape design of a tail-light were defined using the AHP. We followed this by establishing a determination matrix and weight coefficients for the design indicators to create a manual design evaluation model. Second, tail-light shape image datasets were manually annotated based on the evaluation indicators, and design datasets were constructed. The ResNet-50 algorithm was introduced to train the datasets, and the automatic evaluation model for product design was constructed through training and tuning. Finally, we validated the feasibility and effectiveness of the product design evaluation method, which was based on AHP and ResNet-50, by comparing the results obtained using both manual design and automatic design evaluations. The results showed that the proposed sustainable product design evaluation model provides an efficient and reliable method for evaluating product design, improves the decision-making process, and empowers the design and development process. The model enhances resource efficiency and economic sustainability.

Chinese engineering geological named entity recognition by fusing multi-features and data enhancement using deep learning

The engineering geology report serves as a comprehensive portrayal of the geological conditions and information within a surveyed region, making it highly valuable for extracting and mining engineering geology-related knowledge. Geological Named Entity Recognition (GNER), as a pivotal technology for information extraction and knowledge discovery, aims to identify geological objects that convey significant meanings within textual data. While general NER tools and existing approaches are commonly employed for recognizing generic entities, their effectiveness is constrained by the diverse language irregularities inherent in natural language texts, including nested entities, lengthy entities, and a scarcity of domain-specific annotated corpora. Adhering to established standards and principles governing engineering geology reports, we undertake an analysis of text structures and characteristics, as well as the linguistic descriptions and data attributes. By employing an Electronic Design Automation (EDA) enhancement method in conjunction with manual annotation, we construct an engineering GNER dataset. To address these linguistic irregularities, we propose a novel deep learning model that combines both the geological pre-training model (GeoBERT) and multiple features (pinyin, radical, and position vectors) to generate representations from byte sequences. These representations are subsequently fused and passed through a BiLSTM-Attention model for training. Finally, entity classification results are obtained using conditional random fields (CRF). Experimental evaluation demonstrates that the proposed model achieves an impressive F1 value of 79.60% on the constructed datasets, outperforming ten baseline models analyzed in this study.

HG-CAD: Hierarchical Graph Learning for Material Prediction and Recommendation in Computer-Aided Design

Abstract To support intelligent computer-aided design (CAD), we introduce a machine learning architecture, namely HG-CAD, that recommends assembly body material through joint learning of body- and assembly-level features using a hierarchical graph representation. Specifically, we formulate the material prediction and recommendation process as a node-level classification task over a novel hierarchical graph representation of CAD models, with a low-level graph capturing the body geometry, a high-level graph representing the assembly topology, and a batch-level mask randomization enabling contextual awareness. This enables our network to aggregate geometric and topological features from both the body and assembly levels, leading to competitive performance. Qualitative and quantitative evaluation of the proposed architecture on the Fusion 360 Gallery Assembly Dataset demonstrates the feasibility of our approach, outperforming selected computer vision and human baselines while showing promise in application scenarios. The proposed HG-CAD architecture that unifies the processing, encoding, and joint learning of multi-modal CAD features indicates the potential to serve as a recommendation system for design automation and a baseline for future work.

Efficient automatic design of robots

Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior. Despite this, almost every detail of every robot built to date has been manually determined by a human designer after several months or years of iterative ideation, prototyping, and testing. Inspired by evolutionary design in nature, the automated design of robots using evolutionary algorithms has been attempted for two decades, but it too remains inefficient: days of supercomputing are required to design robots in simulation that, when manufactured, exhibit desired behavior. Here we show de novo optimization of a robot's structure to exhibit a desired behavior, within seconds on a single consumer-grade computer, and the manufactured robot's retention of that behavior. Unlike other gradient-based robot design methods, this algorithm does not presuppose any particular anatomical form; starting instead from a randomly-generated apodous body plan, it consistently discovers legged locomotion, the most efficient known form of terrestrial movement. If combined with automated fabrication and scaled up to more challenging tasks, this advance promises near-instantaneous design, manufacture, and deployment of unique and useful machines for medical, environmental, vehicular, and space-based tasks.

Computer aided design automation for triboelectric nanogenerators

For application of triboelectric nanogenerators (TENG), it is essential to design TENG with high energy conversion efficiency, and it is one of the crucial parts to obtain TENG designs with optimal output impedance to match with that of application systems. Current design processes are incapable of optimizing TENGs with complex structures effectively, which greatly prolongs the research and development process and hinders application of TENGs. Here, a universal computer aided design automation is proposed to generate the structural and material design of TENG for optimal power output under designated matching resistance. A semi-analytical method and a non-dominated sorting genetic algorithm (NSGA-II) are used for the design automation. For demonstration, design automation for freestanding rotational TENG (FR-TENG) is carried out. An acceleration factor of up to ∼900 is obtained with systematical simulation acceleration. A group of designs with optimal powers and matching resistances are obtained, from which different input impedances of power management units can be obtained for applications. For verification, a FR-TENG with an optimal average output power over 88 mW is designed and fabricated, the output impedance is intentionally chosen from pareto-optimal front to match the input impedance of the subsequent power management unit. This is used to develop a fully self-powered wireless humidity/temperature/ultraviolet environmental monitoring system. This system can transmit the sensing information every ∼ 7 s and display on a remote terminal, validating the effectiveness of the proposed design automation method.

Discrepancies in the occlusal devices designed by an experienced dental laboratory technician and by 2 artificial intelligence-based automatic programs

Artificial intelligence (AI) models have been developed for different applications, including the automatic design of occlusal devices; however, the design discrepancies of an experienced dental laboratory technician and these AI automatic programs remain unknown. The purpose of this in vitro study was to compare the overall, intaglio, and occlusal surface discrepancies of the occlusal device designs completed by an experienced dental laboratory technician and two AI automatic design programs. Virtually articulated maxillary and mandibular diagnostic casts were obtained in a standard tessellation language (STL) file format. Three groups were created depending on the operator or program used to design the occlusal devices: an experienced dental laboratory technician (control group) and two AI programs, namely Medit Splints from Medit (Medit group) and Automate from 3Shape A/S (3Shape group) (n=10). To minimize the discrepancies in the parameter designs among the groups tested, the same printing material and design parameters were selected. In the control group, the dental laboratory technician imported the articulated scans into a dental design program (DentalCAD) and designed a maxillary occlusal device. The occlusal device designs were exported in STL format. In the Medit and 3Shape groups, the diagnostic casts were imported into the respective AI programs. The AI programs automatically designed the occlusal device without any further operator intervention. The occlusal device designs were exported in STL format. Among the 10 occlusal designs of the control group, a random design (shuffle deck of cards) was used as a reference file to calculate the overall, intaglio, and occlusal discrepancies in the specimens of the AI groups by using a program (Medit Design). The root mean square (RMS) error was calculated. Kruskal-Wallis, and post hoc Dwass-Steel-Critchlow-Fligner pairwise comparison tests were used to analyze the trueness of the data. The Levene test was used to assess the precision data (α=.05). Significant overall (P<.001), intaglio (P<.001), and occlusal RMS median value (P<.001) discrepancies were found among the groups. Significant overall RMS median discrepancies were observed between the control and the Medit groups (P<.001) and the control and 3Shape groups (P<.001). Additionally, significant intaglio RMS median discrepancies were found between the control and the Medit groups (P<.001), the Medit and 3Shape groups (P<.001), and the control and 3Shape groups (P=.008). Lastly, significant occlusal RMS median discrepancies were found between the control and the 3Shape groups (P<.001) and the Medit and 3Shape groups (P<.001). The AI-based software programs tested were able to automatically design occlusal devices with less than a 100-µm trueness discrepancy compared with the dental laboratory technician. The Levene test revealed significant overall (P<.001), intaglio (P<.001), and occlusal (P<.001) precision among the groups tested. The use of a dental laboratory technique influenced the overall, intaglio, and occlusal trueness of the occlusal device designs obtained. No differences were observed in the precision of occlusal device designs acquired among the groups tested.

Image-Based OA-Style Paper Pop-Up Design via Mixed-Integer Programming.

Origami architecture (OA) is a fascinating papercraft that involves only a piece of paper with cuts and folds. Interesting geometric structures 'pop up' when the paper is opened. However, manually designing such a physically valid 2D paper pop-up plan is challenging since fold lines must jointly satisfy hard spatial constraints. Existing works on automatic OA-style paper pop-up design all focused on how to generate a pop-up structure that approximates a given target 3D model. This article presents the first OA-style paper pop-up design framework that takes 2D images instead of 3D models as input. Our work is inspired by the fact that artists often use 2D profiles to guide the design process, thus benefited from the high availability of 2D image resources. Due to the lack of 3D geometry information, we perform novel theoretic analysis to ensure the foldability and stability of the resultant design. Based on a novel graph representation of the paper pop-up plan, we further propose a practical optimization algorithm via mixed-integer programming that jointly optimizes the topology and geometry of the 2D plan. We also allow the user to interactively explore the design space by specifying constraints on fold lines. Finally, we evaluate our framework on various images with interesting 2D shapes. Experiments and comparisons exhibit both the efficacy and efficiency of our framework.

Automatic Scatterplot Design Optimization for Clustering Identification.

Scatterplots are among the most widely used visualization techniques. Compelling scatterplot visualizations improve understanding of data by leveraging visual perception to boost awareness when performing specific visual analytic tasks. Design choices in scatterplots, such as graphical encodings or data aspects, can directly impact decision-making quality for low-level tasks like clustering. Hence, constructing frameworks that consider both the perceptions of the visual encodings and the task being performed enables optimizing visualizations to maximize efficacy. In this article, we propose an automatic tool to optimize the design factors of scatterplots to reveal the most salient cluster structure. Our approach leverages the merge tree data structure to identify the clusters and optimize the choice of subsampling algorithm, sampling rate, marker size, and marker opacity used to generate a scatterplot image. We validate our approach with user and case studies that show it efficiently provides high-quality scatterplot designs from a large parameter space.