Photonics-Aware EDA Tool Market Outlook

According to our latest research, the global photonics-aware EDA tool market size reached USD 1.47 billion in 2024, reflecting a robust momentum in adoption across diverse sectors. The market is expected to grow at a remarkable CAGR of 12.9% from 2025 to 2033, with the forecasted market size anticipated to reach USD 4.36 billion by 2033. This surge is primarily fueled by the escalating demand for integrated photonics in advanced electronics, rapid technological advancements in semiconductor manufacturing, and the increasing complexity of design requirements in next-generation applications.

A critical growth factor propelling the photonics-aware EDA tool market is the accelerating shift towards photonic integrated circuits (PICs) in data-intensive industries. As organizations increasingly rely on high-speed data transmission and energy-efficient processing, the need for precise, simulation-driven design tools that can seamlessly integrate photonic and electronic components has become paramount. The proliferation of artificial intelligence (AI), cloud computing, and 5G networks further amplifies this demand, as these technologies require ultra-fast data communication and low-latency processing—capabilities that only advanced photonics-aware EDA tools can support. Moreover, the ongoing miniaturization of semiconductor devices necessitates sophisticated design automation solutions capable of addressing the unique challenges posed by photonic elements, including signal integrity, thermal management, and crosstalk mitigation.

Another significant driver for the photonics-aware EDA tool market is the surge in research and development activities within the semiconductor and electronics industries. Leading semiconductor manufacturers and research institutes are investing heavily in advanced EDA solutions to accelerate the design and prototyping of photonic devices. These investments are further bolstered by government initiatives and funding programs aimed at fostering innovation in optoelectronics, quantum computing, and next-generation communication systems. The growing collaboration between academia and industry players is also contributing to the evolution of photonics-aware EDA tools, as joint research efforts yield new algorithms, simulation models, and verification techniques that enhance the accuracy and efficiency of photonic circuit design.

The market's growth trajectory is further reinforced by the expanding adoption of photonics in emerging applications such as autonomous vehicles, advanced healthcare diagnostics, and aerospace and defense systems. In the automotive sector, for instance, the integration of photonic sensors and communication modules is pivotal for enabling advanced driver-assistance systems (ADAS) and autonomous driving capabilities. Similarly, in healthcare, photonic technologies are revolutionizing medical imaging, diagnostics, and minimally invasive surgical procedures, necessitating the use of specialized EDA tools for precise device design. The aerospace and defense industry is also leveraging photonics for secure communications, sensing, and navigation, driving additional demand for robust photonics-aware EDA solutions.

Regionally, Asia Pacific stands out as a key growth engine for the photonics-aware EDA tool market, driven by the presence of leading semiconductor manufacturing hubs in China, Japan, South Korea, and Taiwan. North America and Europe also play significant roles, thanks to their strong ecosystem of research institutes, technology innovators, and established semiconductor companies. The Middle East & Africa and Latin America, while currently smaller in market share, are witnessing increasing investments in digital infrastructure and advanced electronics manufacturing, setting the stage for future growth. As the global race for photonic integration intensifies, regional dynamics will continue to shape the competitive landscape and innovation trajectory of the photonics-aware EDA tool market.

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EDA in Industrial Electronic Market Outlook

The global market size for Electronic Design Automation (EDA) in the industrial electronic market stood at approximately USD 10.3 billion in 2023 and is projected to grow to around USD 18.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 6.7% over the forecast period. This robust growth is attributed to increasing demand for advanced electronics in various industrial sectors and the continuous evolution of semiconductor technologies. The integration of EDA tools in streamlining the design and development processes in electronics is further propelling market expansion. The drive towards automation and the demand for precision in industrial electronics stand as significant growth factors for this burgeoning market.

Several factors contribute to the growth of the EDA market in the industrial electronics sector. One of the primary drivers is the escalation of the Internet of Things (IoT) across industrial applications, necessitating sophisticated EDA tools to design efficient semiconductor chips. IoT devices require compact, power-efficient, and high-performance chips, all of which necessitate advanced EDA solutions. Furthermore, the rise of Industry 4.0 and smart manufacturing has increased the complexity of industrial electronics design, thereby heightening the demand for advanced EDA tools to maintain cost-effectiveness and reduce time-to-market. The continuous innovation in semiconductor technology further fuels the need for updated EDA tools, as chip manufacturers seek new ways to meet the dynamic requirements of different industrial applications.

Another growth factor is the increasing adoption of EDA in automotive, aerospace, and defense sectors, driven by the need for sophisticated electronic components. In automotive industries, the push towards electric and autonomous vehicles has heightened the need for advanced electronic systems, which relies heavily on EDA tools for design and verification. Similarly, in aerospace and defense, the demand for high-reliability and performance electronics is crucial, driving the adoption of EDA for design optimization and simulation. These sectors demand precise and reliable electronics, and thus rely increasingly on EDA tools to enhance development processes, design accuracy, and ensure compliance with stringent industry standards.

The proliferation of consumer electronics and the shift towards more personalized and smart devices also contribute notably to the EDA market's growth. The consumer electronics market is characterized by rapid innovation cycles and fierce competition, which necessitate the frequent introduction of new products. This, in turn, creates a demand for efficient EDA tools to accelerate design processes while maintaining high quality and performance standards. Additionally, consumer demand for more features in smaller form factors leads to complex chip designs requiring robust EDA solutions. As a result, manufacturers are leveraging these tools to gain a competitive edge by bringing innovative products to market faster and more efficiently.

Regionally, the EDA market is witnessing significant growth across various geographies, with Asia Pacific standing out as a major contributor. The region's dominance can be attributed to its strong manufacturing base, especially in countries like China, Japan, and South Korea known for their semiconductor and electronics industries. The presence of leading semiconductor companies and a well-established electronics ecosystem further bolster the EDA market in this region. North America and Europe also represent lucrative markets due to their advanced industrial infrastructure and emphasis on technological innovation. In these regions, the focus on research and development, coupled with a high rate of technology adoption, significantly contributes to market growth.

Component Analysis

The component segment in the EDA market is primarily divided into software, hardware, and services. Software remains the most significant segment, driven by the essential role it plays in the design and verification of electronic components. EDA software tools aid in a wide range of functionalities, from circuit simulation to physical verification, enabling designers to optimize their chip designs efficiently. The continuous need for innovation in semiconductor design propels the demand for advanced software solutions, which can handle increasingly complex design requirements. Moreover, the rise in custom silicon designs and application-specific integrated circuits (ASICs) further escalates the demand for sophisticated EDA software tools.

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

TNF ligand ectodysplasin-A1 contributes to embryonic mammary gland development. We searched for target genes of the Eda pathway using profiling of genes differentially expressed in Eda-null mammary buds after a short exposure to recombinant Fc-Eda-A1 protein. Microdissected E13.5 Eda-/- epithelial mammary buds from 5-6 embryos were pooled together. Other half of an embryo was used as a control and the other half as a treated sample. Littermates from 3 litters of B6CBA background were used for the analysis. Array and data analysis were performed in the Biomedicum Functional Genomics Unit (University of Helsinki, Finland).

UAB "EDA Investments" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Electrodermal Activity Detecting Device Market Outlook

According to our latest research, the global Electrodermal Activity Detecting Device market size in 2024 stands at USD 524.7 million, with a robust compound annual growth rate (CAGR) of 11.8% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 1,432.6 million. This impressive growth trajectory is primarily driven by the increasing adoption of wearable health technology, the rising prevalence of stress-related disorders, and the expanding application of electrodermal activity (EDA) devices in both clinical and research settings. As per our latest research, the integration of advanced biosensor technology and the proliferation of digital health platforms are further accelerating the adoption of EDA detecting devices worldwide.

One of the key growth factors propelling the Electrodermal Activity Detecting Device market is the heightened awareness and emphasis on mental health and stress management. With mental health challenges becoming increasingly prevalent in both developed and developing economies, healthcare professionals and consumers alike are seeking innovative, non-invasive tools for real-time monitoring of physiological responses to stress. EDA detecting devices, which measure skin conductance as an indicator of emotional arousal, have emerged as essential tools for biofeedback therapies, stress tracking, and personalized health monitoring. The growing integration of these devices into wellness programs, corporate health initiatives, and even consumer electronics is expanding their reach and utility, creating a fertile ground for market expansion.

Technological advancements in biosensing and miniaturization have further underpinned the growth of the Electrodermal Activity Detecting Device market. Modern wearable and handheld EDA devices are now equipped with sophisticated sensors, wireless connectivity, and user-friendly interfaces, enabling seamless data collection and real-time feedback. The fusion of EDA detection with artificial intelligence and cloud-based analytics is empowering healthcare providers with actionable insights, facilitating early detection of stress-induced health conditions, and supporting personalized treatment regimens. These innovations are not only enhancing the accuracy and reliability of EDA measurements but are also making these devices more accessible for homecare and remote monitoring applications, thereby increasing their adoption across diverse end-user segments.

The expanding application landscape of EDA detecting devices is another significant driver of market growth. Beyond traditional healthcare and clinical research, these devices are finding new uses in areas such as sports performance monitoring, consumer wellness, and human-computer interaction studies. The ability of EDA sensors to provide objective physiological feedback is fostering their adoption in behavioral research, neuromarketing, and even gaming. As the ecosystem of digital health and biofeedback solutions continues to evolve, the Electrodermal Activity Detecting Device market is poised to benefit from a surge in demand from both institutional and individual users, further reinforcing its upward growth trajectory.

Regionally, North America remains the dominant market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The widespread adoption of digital health technologies, strong healthcare infrastructure, and the presence of leading market players are key factors contributing to North America's leadership. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by increasing healthcare investments, rising consumer awareness, and rapid technological adoption. Europe, with its robust research ecosystem and supportive regulatory environment, continues to be a significant market for EDA devices, particularly in clinical and academic applications.

Product Type Analysis

The Electrodermal Activity Detecting Device market is segm

We all love movies! I remember watching my first movie with my family when I was 5 and 3 years later, I still love movies. But have you ever wondered how some people rate movies as good or bad, awesome or mehh! That's correct. Different people have different perspectives on how they like or dislike movies. To help us select from a plethora of movie option out there, IMDB platform provides us honest reviews by the people for the people.

Long story short, this assignment will take you through different aspects of how a movie is reviewed by different people from across the globe based on their star cast, genre, story length and many more aspects.

So here is what you need to do! Few points: 1. Download the dataset & the dictionary that will help you learn the different columns in the dataset 2. Start exploring the data by performing EDA (wiki what’s EDA, if you are a dummy like I was initially) 3. Get back to this notebook to check what all I did for exploring through the data and then follow the subtasks & checkpoints!

Simple? Isn’t it! Do complete the exercise & let me know in the comments if you found this exercise helpful? There’s always a scope for improvement. Tell me what more could have been added to this notebook! Hope you’ll have a good time exploring data.

Numavičiaus įmonė "EDA" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

This dataset contains the digitized treatments in Plazi based on the original journal article Yamasaki, Takeshi, Eda, Masaki, Schodde, Richard, Loskot, Vladimir (2022): Neotype designation of the Short-tailed Albatross Phoebastria albatrus (Pallas 1769) (Aves: Procellariiformes: Diomedeidae). Zootaxa 5124 (1): 81-87, DOI: 10.11646/zootaxa.5124.1.6

Allele frequencies in microsatellite loci Stn380 and Stn381 which are strongly linked to the EDA gene [34]. For analysis the alleles of Stn380 were simplified to two genotypes: Long (>190 bps) and Short (

Uždaroji akcinė bendrovė "Eda" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

The dataset contains the file required for training and testing and split accordingly.

There are two groups of features that you can use for prediction:

1. Fundamentals and ratios: Values collected form statements and balance sheets for each ticker
2. Technical indicators and strategy flags: Technical indicators calculated on close value of each day and buy and sell signals generated using some commonly used trading strategies.

Files found in Fundamentals folder is a processed format of the files found in raw folder. Ratios and other values are stretched to match the length of the closing price column such that the value in the pe_ratio column for example is the PE ratio from the most recent quarter and this applies for every column.

Technical indicators are calculated with the default parameters used in Pandas_TA package.

Data is collected form finance.yahoo.com and macrotrends.net Timeframe for the given data is different from one ticker to another because of unavailability of some stocks for a given time frame on either of the websites.

All code required to collect the data and perform preprocessing and feature engineering to get the data in the given format can be found in the following notebooks:

1. https://www.kaggle.com/code/mohammedobeidat/us-stocks-data-collection
2. https://www.kaggle.com/code/mohammedobeidat/us-stocks-technicals-feature-engineering-and-eda
3. https://www.kaggle.com/code/mohammedobeidat/us-stocks-fundamentals-preprocessing-and-eda

Files

• {<>_ticker_train}.csv - the training set
• {<>_ticker_train}.csv - the test set

Columns

Columns names are supposed to be self-explanatory assuming you are familiar with the stock market. Some acronyms you may encounter:

1. tmm is short for Trailing Twelve Months
2. pe is short for Price to Earnings
3. pb is short for Price to Book Value
4. ps is short for Price to Sales
5. fcf is short for Free Cash Flow
6. eps is short for Earnings per Share

F1 Dataset for the 2022 season

This dataset contains the driver statistics of every driver for the F1 2022 season. Analyzing F1 data has always been my passion and I wanted to share it with everyone. As I was scrolling the F1 official website, I couldn't find a comprehensive compilation of all the driver related statistics at one place.

So, I decided to do it myself.

The data that you will see in the csv file is entirely web-scraped by me and open for you to use it however you want.

For Sports and F1 enthusiasts

You can use this data for EDA, visualizations as well as build models to predict the 2023 season winner

This dataset is quite short as it has data of only 2022 season and is intended towards beginners in the field of Data Science

Žilvino Gaižausko įmonė "Eda" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Directory content: This directory contains 21 .CSV files with electrodermal activity (EDA), electrocardiogram (ECG), blood volume pulse (BVP), and respiration data (recorded at 400 Hz) for each driver (i.e., ID_Number_BiosignalsData). All files were pre-processed in two steps to ensure the correct sampling rate: (i) removing lines with timestamps repetitions, and (ii) resampled to 400 Hz using regularly spaced and linear interpolation. This directory also includes a .CSV file reporting the data description (Legend_DataBiosignal).Method and instruments: We used a BiosignalsPlux Research Kit (PLUX Wireless Biosignals, Lisbon, Portugal) to monitor participants’ ECG, BVP, EDA, and respiration data. The BiosignalsPlux system includes a wearable hub with an 8-channel configuration (analog ports) of 16-bit per channel resolution, using Bluetooth data transmission technology for synchronization with the driving simulator.We used disposable, self-adhesive, pre-gelled Ag/AgCl electrodes (24 mm diameter) for the ECG and EDA measurements. The EDA was recorded employing a dedicated single-lead local differential bipolar DC sensor (0-3 Hz bandwidth, 0-100 µS range), with two leads (a positive and a negative lead, 5.0 ± 0.5 cm length each), each one ending with a dedicated electrode socket. Once we cleaned the skin with an alcohol-free disinfectant, we placed the electrodes on the thenar (negative electrode) and hypothenar (positive electrode) eminences of the left hand. We made sure to let enough space on the hand palm between the two electrodes to minimize the risk of signal artifacts due to the pressure of the hand on the steering wheel. The ECG was recorded with a single-lead local differential bipolar sensor (0.5-100 Hz bandwidth, ± 1.47 mV range), including a positive, a negative, and a reference cable, each one ending with a dedicated electrode socket. Once we cleaned the skin, we placed the electrodes on the participant’s chest (Lead II configuration): one electrode on the depression below each of the shoulder blades (reference on the left side, positive on the right side) and one electrode (negative) on the fifth intercostal space of the left side.The BVP was measured through an optical, non-invasive ear-clip sensor (0.02-2.1 Hz bandwidth, 535±10 nm centroid wavelength), including a light emitter (LED) and detector. The sensor (LED and detector) was placed at the center of the left ear lobe.The respiration data was recorded using an elastic, adjustable chest belt that included a piezoelectric sensor (0.059-1 Hz bandwidth, ± 1.50 V range). We placed the belt on the participant’s chest, over a cotton short-sleeve T-shirt, ∼2 cm below the pectoral muscles, and connected to the hub using a dedicated cable of about 110 cm total length.

Machine learning techniques are quite effective for simulating species habitat appropriateness. Species distribution models are statistical algorithms founded on the ecological niche idea. These models estimate the association between existing species records and the environmental and spatial characteristics of the habitat. From 2022 to 2023, field survey was conducted in the Kastamonu Forest Enterprise, resulting in the identification of 267 active Formica rufa nests. The habitat preferences of F. rufa were assessed based on factors such as stand characteristics, topography, and climatic variables. MaxEnt, a prevalent machine learning technique for predicting species habitat suitability, was employed in the habitat suitability modeling of Formica rufa. 30 distinct variables were employed in the modeling process. Receiver Operating Characteristic (ROC) analysis examined model accuracy. AUC was 0.941 for training data and 0.946 for test data. With 39.5% of the model, the development stage is the most important variable for F. rufa habitat selection. The development stage, productivity, and temperature annual range (BIO7) variables make up 75.1% of the model. The habitat suitability map shows that 79% of F. rufa nests are in moderately and highly appropriate areas. The Formica rufa group, widely prevalent in northern hemisphere forests, significantly impacts forest ecosystems and is recognized as the foremost bioindicator species within these environments. Determining the elements that affect habitat selection by these species is essential for their conservation and management.

Synthetic dataset for macro placement, generated using ArtNetGen and Cadence EDA tool.

Analysis of ‘Census Block Group Economically Distressed Areas 2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ac57065c-1179-421b-968f-e8010700189c on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This is a copy of the statewide Census Block Group GIS Tiger file. It is used to determine if a block group (BG) is EDA or not by adding ACS (American Community Survey) Median Household Income (MHI) and Population Density data at the BG level. The IRWM web based DAC mapping tool uses this GIS layer. Every year this table gets updated after ACS publishes their updated estimates. Created by joining 2016 EDA table to 2010 block groups feature class. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas.

--- Original source retains full ownership of the source dataset ---

UAB "EDA statyba" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.