ZVEI – Daniel Hager is the association’s new President

EMR Analysis

More information on ZVEI: See the full profile on EMR Executive Services

More information on Wolfgang Weber (Chief Executive Officer and Chairman of the Executive Board, ZVEI): See the full profile on EMR Executive Services

More information on Daniel Hager (Chairman of the Supervisory Board, Hager Group + President, ZVEI + Member of the Extended Board, ZVEI + Chairman of the ZVEI Buildings Platform, ZVEI + Member of the Electrical Installation Systems Division, ZVEI + Member of the Board of Directors, Eiffage): See the full profile on EMR Executive Services

 

More information on eSummit 2026 by ZVEI (May 20th and 21st, 2026 – EUREF Campus, Berlin, Germany): https://www.esummit.zvei.org/ + The eSummit 2026 on May 20th and 21st at the EUREF Campus in Berlin is the summit meeting of the electrical and digital industries.

Artificial intelligence has the potential to become a key driver of growth for our economy. Whether in industrial applications or in increasingly smart, networked devices and services: the electrical and digital industries are driving industrial digitalization forward and thus the next stage of Industry 4.0.

Our goal is clear:  Future made with Germany.

How can artificial intelligence not only be developed in Germany, but also brought into widespread use? What political and regulatory frameworks are needed so that politics and business can jointly drive innovation and consider technological excellence, competitiveness, and sovereignty together?

 

 

 

More information on Friedrich Merz (Federal Chancellor, Germany): https://www.bundesregierung.de/breg-en/federal-cabinet/cv-2343398 + https://www.linkedin.com/in/friedrich-merz/ 

 

 

 

More information on The European Union: https://european-union.europa.eu/index_en + The European Union’s institutional set-up is unique and its decision-making system is constantly evolving. The 7 European institutions, 7 EU bodies and over 30 decentralized agencies are spread across the EU. They work together to address the common interests of the EU and European people. 

In terms of administration, there are a further 20 EU agencies and organisations which carry out specific legal functions and 4 interinstitutional services which support the institutions.

All of these establishments have specific roles – from developing EU laws and policy-making to implementing policies and working on specialist areas, such as health, medicine, transport and the environment.

There are 4 main decision-making institutions which lead the EU’s administration. These institutions collectively provide the EU with policy direction and play different roles in the law-making process: 

• The European Parliament (Brussels/Strasbourg/Luxembourg)
• The European Council (Brussels)
• The Council of the European Union (Brussels/Luxembourg)
• The European Commission (Brussels/Luxembourg/Representations across the EU)

Their work is complemented by other institutions and bodies, which include:

• The Court of Justice of the European Union (Luxembourg)
• The European Central Bank (Frankfurt)
• The European Court of Auditors (Luxembourg)

The EU institutions and bodies cooperate extensively with the network of EU agencies and organisations across the European Union. The primary function of these bodies and agencies is to translate policies into realities on the ground.

Around 60,000 EU civil servants and other staff serve the 450 million Europeans (and countless others around the world).

Currently, 27 countries are part of the EU: https://european-union.europa.eu/principles-countries-history/country-profiles_en 

 

More information on The European Commission by The European Union: https://ec.europa.eu/info/index_en + The Commission helps to shape the EU’s overall strategy, proposes new EU laws and policies, monitors their implementation and manages the EU budget. It also plays a significant role in supporting international development and delivering aid.

The Commission is steered by a group of 27 Commissioners, known as ‘the college’. Together they take decisions on the Commission’s political and strategic direction.

A new college of Commissioners is appointed every 5 years.

The Commission is organised into policy departments, known as Directorates-General (DGs), which are responsible for different policy areas. DGs develop, implement and manage EU policy, law, and funding programmes. In addition, service departments deal with particular administrative issues. Executive agencies manage programmes set up by the Commission.

Principal roles in law: The Commission proposes and implements laws which are in keeping with the objectives of the EU treaties. It encourages input from business and citizens in the law-making process and ensures laws are correctly implemented, evaluated and updated when needed.

More information on Ursula von der Leyen (President, The European Commission, The European Union): https://ec.europa.eu/commission/commissioners/2019-2024/president_en + https://www.linkedin.com/in/ursula-von-der-leyen/ 

 

More information on the EU AI Act by The European Commission by The European Union: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai + https://artificialintelligenceact.eu/ + The AI Act is the first-ever legal framework on AI, which addresses the risks of AI and positions Europe to play a leading role globally.

The AI Act (Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence) is the first-ever comprehensive legal framework on AI worldwide. The aim of the rules is to foster trustworthy AI in Europe. For any questions on the AI Act, check out the AI Act Single Information platform.

The AI Act sets out a risk-based rules for AI developers and deployers regarding specific uses of AI. The AI Act is part of a wider package of policy measures to support the development of trustworthy AI, which also includes the AI Continent Action Plan, the  AI Innovation Package and the launch of AI Factories. Together, these measures guarantee safety, fundamental rights and human-centric AI, and strengthen uptake, investment and innovation in AI across the EU.

To facilitate the transition to the new regulatory framework, the Commission has launched the AI Pact, a voluntary initiative that seeks to support the future implementation, engage with stakeholders and invite AI providers and deployers from Europe and beyond to comply with the key obligations of the AI Act ahead of time. In parallel, the AI Act Service Desk is also providing information and support for a smooth and effective implementation of the AI Act across the EU.

More information on the EU AI Omnibus by The European Commission by The European Union: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52025PC0836 + The Digital Omnibus on AI (commonly known as the AI Omnibus) is a targeted legislative package finalized by the European Union on May 7, 2026, that amends the landmark EU AI Act. Rather than serving as a standalone law, it acts as a “course correction” to streamline obligations, eliminate bureaucratic duplication, and prevent the AI Act from stifling industrial growth and European competitiveness.

 

 

 

More information on Hager Group: See the full profile on EMR Executive Services

More information on Sabine Busse (Group Chief Executive Officer, Hager Group): See the full profile on EMR Executive Services

 

 

 

More information on Siemens AG: See full profile on EMR Executive Services

More information on Dr. Roland Busch (President and Chief Executive Officer, Siemens AG): See full profile on EMR Executive Services

More information on Dr. Peter Körte (Member of the Managing Board and Chief Technology and Chief Strategy Officer with responsibility for Siemens Xcelerator and Siemens Advanta, Siemens AG + Member of the Managing Board and Chief Executive Officer, Siemens Smart Infrastructure (SI), Siemens AG as from July 1, 2026 + Vice-President, ZVEI): See the full profile on EMR Executive Services

 

 

 

More information on Robert Bosch GmbH: https://www.bosch.com/ + The Bosch Group is a leading global supplier of technology and services. It employs roughly 417,900 associates worldwide (as of December 31, 2024). According to preliminary figures, the company generated sales of 90.5 billion euros in 2024. Its operations are divided into four business sectors: Mobility, Industrial Technology, Consumer Goods, and Energy and Building Technology. With its business activities, the company aims to use technology to help shape universal trends such as automation, electrification, digitalization, connectivity, and an orientation to sustainability. In this context, Bosch’s broad diversification across regions and industries strengthens its innovativeness and robustness. Bosch uses its proven expertise in sensor technology, software, and services to offer customers cross-domain solutions from a single source. It also applies its expertise in connectivity and artificial intelligence in order to develop and manufacture user-friendly, sustainable products. With technology that is “Invented for life,” Bosch wants to help improve quality of life and conserve natural resources.

• 417,900 associates worldwide
• 90.5 billion euros sales revenue in 2024
• 468 subsidiaries and regional companies in some 60 countries
• 3.2 billion euros EBIT

More information on Dr. Stefan Hartung (Chairman of the Board of Management, Robert Bosch GmbH):  https://www.bosch.com/company/our-people/#board-of-management + https://www.linkedin.com/in/dr-stefan-hartung/

More information on Dr. Frank Meyer (Member of the Board of Management, Head of Bosch Energy and Building Technology Business Sector, Robert Bosch GmbH + Vice-President, ZVEI): See the full profile on EMR Executive Services

 

 

 

More information on Schneider Electric: See the full profile on EMR Executive Services

More information on Olivier Blum (Chief Executive Officer, Schneider Electric): See the full profile on EMR Executive Services

More information on Peter Weckesser (Member of the Executive Committee and Chief Digital Officer, Schneider Electric + Vice-President, ZVEI): See the full profile on EMR Executive Services 

 

 

 

More information on Kostal: https://www.kostal-kontakt-systeme.com/en/ + KOSTAL Kontakt Systeme GmbH & Co. KG is an independent company in the KOSTAL Group, a family company acting internationally and counting all the world’s leading automobile manufacturers among its customers. The core business of KOSTAL Kontakt Systeme GmbH & Co. KG is the development, production and sale of plug connector systems for the automobile industry. KOSTAL Kontakt Systeme GmbH & Co. KG has a presence on three continents, with nine locations and 1400 employees.

More information on Dr. Markus Bergholz (Chief Executive Officer, Kostal Systemes GmbH & Co. KG, Kostal Group + Member of the Executive Committee, ZVEI): See the full profile on EMR Executive Services

 

 

 

More information on Erbe Elektromedizin: https://en.erbegroup.com/en-en/ + Since 1851 – a strong and innovative group of companies. As a family-owned and operated business, Erbe develops, manufactures, and markets medical technology systems for professional use across various disciplines worldwide.

Since its founding in 1851, the company has continuously evolved, adapting to changing clinical needs in fields such as gastroenterology, general surgery, gynecology, pulmonology, and urology. The product portfolio includes devices and instruments for electrosurgery, vessel sealing, plasma surgery, cryotechnology, hydrosurgery, and imaging.

Over the past ten years, sales have increased by 75%, and the workforce has more than doubled to over 2,000 employees globally. Today, the group is represented in 110 countries through its own sales and service units, production sites, and an international distributor network.

A team of more than 300 employees is dedicated to research and development. Close collaboration with leading physicians from medical faculties and hospitals plays a key role in helping Erbe drive medical innovation forward.

More information on Christian O. Erbe (Chief Executive Officer, Erbe Elektromedizin + Member of the Executive Committee, ZVEI): See the full profile on EMR Executive Services

 

 

 

More information on HARTING: See the full profile on EMR Executive Services

More information on Philip F.W. Harting (Chairman of the Board of Management, HARTING Technology Group + Chairman, AUMA + Member of the Executive Committee, ZVEI): See the full profile on EMR Executive Services

 

 

 

More information on Siemens Energy AG: See the full profile on EMR Executive Services

More information on Dr. -Ing. Christian Bruch (President and Chief Executive Officer, Siemens Energy AG + President and Chief Executive Officer, Siemens Energy Management GmbH + Chief Sustainability Officer, Siemens Energy AG + Export Control, Siemens Energy AG): See the full profile on EMR Executive Services

More information on Tim Oliver Holt (Member of the Executive Board Responsible for Grid Technologies Business Area and Labor Director, Siemens Energy AG + Member of the Executive Board and Labor Director, Siemens Energy Management GmbH, Siemens Energy AG + Member of the Executive Committee, ZVEI): See the full profile on EMR Executive Services

 

 

 

More information on Phoenix Contact: See the full profile on EMR Executive Services

More information on Dirk Görlitzer (Chairman of the Executive Board and Chief Executive Officer, Phoenix Contact): See the full profile on EMR Executive Services

More information on Ulrich Leidecker (Member and Spokesman of the Group Executive Board, Chief Operating Officer and – President of the Board of the Industry Management and Automation (IMA) Business Area, Phoenix Contact + Member of the Executive Committee, ZVEI): See the full profile on EMR Executive Services

 

 

 

More information on Wöhner: https://www.woehner.de/en/ + For over 90 years, our company has stood for future-oriented technologies in the distribution and control of electrical energy. Our success story began with the foundation of the company by Alfred Wöhner in 1929 and his innovative capacity as a driving force.

Today, the Wöhner Group is established worldwide as a specialist for international fuse and busbar systems in the field of power distribution, control technology and renewable energies.

In conjunction with our 12 subsidiaries and an extensive network of representatives in Germany and internationally, we look after customers in over 80 countries. A talent for invention, the consistent implementation of our system concept and continuous growth are the cornerstones of our success.

With over 100 registered patents, we have established ourselves internationally as a forward-looking company and as a strong brand. During this process, we have always remained true to our roots: Our products are still manufactured in Germany today.

Our family business has its headquarters in the Upper Franconian town of Rödental near Coburg (Bavaria); the chairman of the management board is Frank Wöhner, the grandson of the founder of the company.

More information on Philipp Steinberger (Chief Executive Officer, Wöhner + Member of the Executive Committee, ZVEI): See the full profile on EMR Executive Services

 

 

 

More information on Infineon Technologies AG: https://www.infineon.com + Infineon Technologies AG is a global semiconductor leader in power systems and IoT. Infineon drives decarbonization and digitalization with its products and solutions. The Company had around 57,000 employees worldwide (end of September 2025) and generated revenue of about €14.7 billion in the 2025 fiscal year (ending 30 September). Infineon is listed on the Frankfurt Stock Exchange (ticker symbol: IFX) and in the USA on the OTCQX International over-the-counter market (ticker symbol: IFNNY).

More information on Jochen Hanebeck (Chief Executive Officer, Infineon Technologies AG): https://www.infineon.com/about/company/leadership-team  + https://www.linkedin.com/in/jochen-hanebeck-19987713/ 

More information on Andreas Urschitz (Chief Marketing Officer, Infineon Technologies AG + Member of the Executive Committee, ZVEI): See the full profile on EMR Executive Services

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• AI – Artificial Intelligence:
  • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
  • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but several, including Python, R and Java, are popular.
  • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
  • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
  • The 4 types of artificial intelligence?
    • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
    • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
    • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
    • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
  • Machine Learning (ML):
    • Developed to mimic human intelligence, it lets the machines learn independently by ingesting vast amounts of data, statistics formulas and detecting patterns.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • ML algorithms use historical data as input to predict new output values.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
    • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
  • Deep Learning (DL):
    • Subset of machine learning, Deep Learning enabled much smarter results than were originally possible with ML. Face recognition is a good example.
    • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Generative AI (GenAI):
    • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.
  • Small Language Models (SLM) and Large Language Models (LLM):
    • Small Language Models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
    • LLM means Large Language Models — a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
    • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Field of AI that enables computers to interpret and act on visual data (images, videos). It works by using deep learning models trained on large datasets to recognize patterns, objects, and context.
    • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV) :
      • Specific application for industrial settings, relying on cameras to analyze tasks in manufacturing, quality control, and worker safety. The key difference is that CV is a broader field for extracting information from various visual inputs, while MV is more focused on specific industrial tasks.
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
      • MV uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
  • Multimodal Intelligence and Agents:
    • Subset of artificial intelligence that integrates information from various modalities, such as text, images, audio, and video, to build more accurate and comprehensive AI models.
    • Multimodal capabilities allows AI to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
    • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
  • Agentic AI:
    • Agentic AI is an artificial intelligence system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multiagent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration.
    • Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
    • Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals.
  • Edge AI Technology:
    • Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
    • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
    • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
  • High-Density AI: 
    • High-density AI refers to the concentration of AI computing power and storage within a compact physical space, often found in specialized data centers. This approach allows for increased computational capacity, faster training times, and the ability to handle complex simulations that would be impossible with traditional infrastructure.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI): 
    • Explainable AI (XAI) refers to methods for making AI model decisions understandable to humans, focusing on how the AI works, whereas Human-Centered Explainable AI (HCXAI) goes further by contextualizing those explanations to a user’s specific task and understanding needs. While XAI aims for technical transparency of the model, HCXAI emphasizes the human context, emphasizing user relevance, and the broader implications of explanations, including fairness, trust, and ethical considerations.
  • Physical AI & Embodied AI: 
    • Physical AI refers to a branch of artificial intelligence that enables machines to perceive, understand, and interact with the physical world by directly processing data from a variety of sensors and actuators.
    • Physical AI provides the overarching framework for creating autonomous systems that act intelligently in real-world settings. Embodied AI, as a subset, focuses on the sensory, decision-making, and interaction capabilities that enable these systems to function effectively in dynamic and unpredictable environments.
  • Federated Learning and Reinforcement Learning:
    • Federated Learning is a machine-learning technique where data stays where it is, and only the learned model updates are shared. “Training AI without sharing your data”.
    • Reinforcement Learning is a type of AI where an agent learns by interacting with an environment and receiving rewards or penalties. “Learning by trial and error”
    • Federated Learning (FL) and Reinforcement Learning (RL) can be combined into a field called Federated Reinforcement Learning (FRL), where multiple agents learn collaboratively without sharing their raw data. In this approach, each agent trains its own RL policy locally and shares model updates, like parameters or gradients, with a central server. The server aggregates these updates to create a more robust, global model. FRL is used in applications like optimizing resource management in communication networks and enhancing the performance of autonomous systems by learning from diverse, distributed experiences while protecting privacy.
  • AI Factories:
    • AI Factories are specialized, high-performance computing centers designed to train, tune, and deploy artificial intelligence models at scale.
    • Companies and organizations involved in AI factory infrastructure and development include Nvidia, AWS, Microsoft, OpenAI, CoreWeave, Lambda, Nebius, Supermicro, and HPE. The European Union is also establishing AI Factories through its EuroHPC Joint Undertaking to foster regional innovation.

 

 

• Microelectronics:
  • Microelectronics is a subfield of electronics focused on designing, developing, and manufacturing highly miniaturized electronic components and circuits, primarily integrated circuits (microchips). It is the foundational technology that powers all digital applications, ranging from smartphones and AI to electric vehicles and medical equipment.
  • Microelectronics is a “key enabling technology” vital for Europe’s digitalization, climate goals, and technological sovereignty.

 

 

• Grid, Microgrids, DERs and DERM’s:
  • Grid / Power Grid:
    • The power grid is a network for delivering electricity to consumers. The power grid includes generator stations, transmission lines and towers, and individual consumer distribution lines.
      • The grid constantly balances the supply and demand for the energy that powers everything from industry to household appliances.
      • Electric grids perform three major functions: power generation, transmission, and distribution.
  • Microgrid:
    • Small-scale power grid that can operate independently or collaboratively with other small power grids. The practice of using microgrids is known as distributed, dispersed, decentralized, district or embedded energy production.
  • Smart Grid:
    • Any electrical grid + IT at all levels.
  • Micro Grid:
    • Group of interconnected loads and DERs (Distributed Energy Resources) within a clearly defined electrical and geographical boundaries witch acts as a single controllable entity with respect to the main grid.
  • Distributed Energy Resources (DERs): 
    • Small-scale electricity supply (typically in the range of 3 kW to 50 MW) or demand resources that are interconnected to the electric grid. They are power generation resources and are usually located close to load centers, and can be used individually or in aggregate to provide value to the grid.
      • Common examples of DERs include rooftop solar PV units, natural gas turbines, microturbines, wind turbines, biomass generators, fuel cells, tri-generation units, battery storage, electric vehicles (EV) and EV chargers, and demand response applications.
  • Distributed Energy Resources Management Systems (DERMS):
    • Platforms which helps mostly distribution system operators (DSO) manage their grids that are mainly based on distributed energy resources (DER).
      • DERMS are used by utilities and other energy companies to aggregate a large energy load for participation in the demand response market. DERMS can be defined in many ways, depending on the use case and underlying energy asset.

 

 

• Semiconductor:
  • Solid substance that has a conductivity between that of an insulator and that of most metals, either due to the addition of an impurity or because of temperature effects. Devices made of semiconductors, notably silicon, are essential components of most electronic circuits. Some examples of semiconductors are silicon, germanium, gallium arsenide, and elements near the so-called “metalloid staircase” on the periodic table. … Silicon is a critical element for fabricating most electronic circuits.
• Semiconductor Wafer:
  • A semiconductor wafer is a thin, circular slice of a semiconductor material, most commonly silicon, that serves as the foundation for creating integrated circuits and other microelectronic devices. These wafers are made from highly pure, single-crystal material and undergo numerous processing steps to build the complex circuitry of chips.
• SiC Semi-Conductor Technology:
  • Silicon carbide (SiC), a semiconductor compound consisting of silicon (Si) and carbon (C), belongs to the wide bandgap (WBG) family of materials. Its physical bond is very strong, giving the semiconductor a high mechanical, chemical and thermal stability.
  • Silicon carbide, exceedingly hard, synthetically produced crystalline compound of silicon and carbon. Its chemical formula is SiC. Since the late 19th century silicon carbide has been an important material for sandpapers, grinding wheels, and cutting tools.
  • Since there is less energy to dissipate, an SiC device can switch at higher frequencies and improve efficiency. The higher efficiency, smaller size and lower weight of SiC can create a higher-rated solution or a smaller design with reduced cooling requirements.
• Silicon Power Semiconductor:
  • Silicon power devices are defined as semiconductor components used for controlling and managing electrical power, capable of handling various voltage and current levels across a wide range of applications, including high voltage direct current (HVDC) power distribution, automotive electronics, and industrial motor drives. These devices include power MOSFETs and IGBTs, which are preferred for their efficiency and high performance in different frequency and power settings.