The year to date shows that excessive dependency harbors risks. Increasing political uncertainty and high valuations are prompting many investors to look for alternatives. Europe and Asia offer exciting companies that are often valued more favorably and have great long-term potential.

Strong European alternatives for your portfolio:

Adyen $ADYEN (-5.35%) is a leading payment service provider that is benefiting from increasing digitalization. After a difficult year, the company could get back on track.

Schneider Electric $SU (-8.15%) from France is a key player in energy and automation technology and is benefiting from electrification and the growing focus on sustainability.

Novo Nordisk $NOVO B (-6.66%) a classic and dominates the market for diabetes and obesity medication. The strong demand for Wegovy and Co. ensures continuous growth.

ASML $ASML (-2.09%) is indispensable for the chip industry. Without ASML's machines, there would be no modern semiconductors. A real growth stock for the future.

Lotus Bakeries $LOTB (-2.66%) is growing worldwide with its popular Biscoff cookies. The expansion into new markets makes the company exciting for long-term investors. More on this in one of my last posts.

Exciting stocks from Asia:

Tokyo Electron $8035 (-6.71%) is one of the most important suppliers to the semiconductor industry and is benefiting from the global chip boom.

Alibaba $9988 (-9.39%) remains an e-commerce and cloud giant with long-term potential despite regulatory challenges.

Fast Retailing $9983 (-3.48%) (Uniqlo) is growing strongly in Asia and could establish itself as a global fashion brand.

The MSCI World ex USA as an alternative for passive investors:

If you want to reduce your US share but do not want to invest in individual stocks, you can use an ETF on the MSCI World ex USA as an alternative. Regular purchases via a savings plan can gradually dilute the US share in the portfolio.

What is your current US share? Are you planning to reallocate or are you still heavily invested in the USA?

#etfs
#stockanalysis
#tech
#dividends
#growth
#usa
#eu
#asien

$ASML (-2.09%)
$8035 (-6.71%) . $NVDA (-7.25%)

Donald Trump's government is planning to further tighten export restrictions on semiconductors to China.

In doing so, it would not only continue the measures of the Biden administration, but expand them considerably, reports Bloomberg. The aim is to slow down China's technological progress and involve key allies in the US strategy.

US officials recently met with representatives from Japan and the Netherlands to discuss new restrictions on companies such as Tokyo Electron and ASML. Specifically, the aim is to prevent engineers from these companies from maintaining semiconductor production facilities in China. This could significantly affect the production capacities of Chinese chip manufacturers.

Chinese semiconductor shares are already pulling their heads in. The FactSet China Semiconductor Index loses a whole percentage point, which does not seem too tragic given that the index is up around 50 percent for the year:

In addition, the Trump camp is aiming to further reduce the amount and type of Nvidia chips that can be shipped to China without an export license.

The Trump administration's strategy aims to have close US allies introduce export restrictions similar to those already in place for US chipmakers such as Lam Research, KLA and Applied Materials. Such a move could put further pressure on China's semiconductor industry and increase its dependence on domestic alternatives.

https://www.wallstreet-online.de/nachricht/19041012-eskalationsstufe-trump-untersagt-ingenieuren-chinesische-halbleiter-anlagen-warten

Graphic: AI generated

The rapid development of AI has triggered a veritable boom in data centers. Companies such as OpenAI and DeepSeek are driving this revolution and the demand for high-performance servers is growing exponentially.

However, the increase in computing power is also accompanied by massive energy consumption, an issue that is leading to global discussions about infrastructure, efficiency and future investments [1].

At the same time, the question arises as to whether there is currently an overinvestment in computing power. The Chinese AI company DeepSeek, for example, has presented a model that works more efficiently than previous large language models (LLMs).

Does this mean that we will soon need less computing power?

Or will the Jevons paradox occur instead, i.e. the effect that more efficient technologies actually increase overall consumption in the long term? [2, 3]

In this article, I will focus on the key developments in the data center sector, the growing demand for energy, regional characteristics, current challenges and potential investment opportunities.

As always, the article is intended to shed light on the background to current events, provide food for thought and give impetus. The stocks mentioned do not, of course, constitute investment advice.

🤖 Data centers: the foundation of the AI revolution

The growing global demand for AI-supported software and digital applications requires powerful data centers. Goldman Sachs analysts forecast that the global demand for power from data centers will increase by 50% by 2027 and by up to 165 % could increase [1].

This chart below forecasts the energy consumption of data centers (in terawatt hours) by 2030, distinguishing between AI- and non-AI-based applications in the US and the rest of the world. Total consumption is expected to rise to over 1,000 TWh by 2030 [4].

Our analysts expect data center power consumption to increase by more than 160% by 2030

Source: [4], primary: Masanet et al. (2020), Cisco, IEA, Goldman Sachs Research

This data shows how AI applications will massively increase energy consumption. The rapid increase in the area of "US AI" and "Rest of world AI" is particularly striking.

The three main reasons for this increase are

• Larger AI models:

New models such as GPT-5 or DeepSeek AI require more and more computing power. Training and operating these models requires trillions of calculations [1].

• Real-time AI applications:

Companies are integrating AI into numerous applications: from search engines to personalized financial and healthcare services.

• Cloud computing & data storage:

As digitalization progresses, the global demand for data storage and cloud services is increasing [1].

Which companies dominate the market?

On the demand side for data centers, large hyperscale cloud providers and other companies are building large language models (LLMs) that are capable of processing and understanding natural language. These models need to be trained on huge amounts of information using power-intensive processors [4].

On the supply side, hyperscale cloud companies, data center operators and asset managers are deploying large amounts of capital to build new high-capacity data centers.

These include, among others:

• Microsoft $MSFT (-2.36%) : Operator of Azure Cloud and partner of OpenAI
• Alphabet $GOOGL (-2.74%) : With Google Cloud and DeepMind
• Amazon $AMZN (-3.47%) : AWS, the world's leading cloud provider
• Meta $META (-4.44%) : Develops its own AI chips and continues to expand its infrastructure

In addition, specialized data center providers such as Equinix $EQIX (-3.33%) and Digital Realty $DLR (+0.82%) as they supply physical infrastructure to the hyperscalers [6].

According to Goldman Sachs Research, demand for data center infrastructure will increasingly outstrip supply in the coming years.

The utilization rate of existing data centers is expected to rise from around 85% in 2023 to more than 95% by the end of 2026. However, the situation is expected to ease from 2027 onwards as new data centers are commissioned and demand growth driven by AI slows down (see chart below) [1].

Goldman Sachs currently estimates that the global power consumption of the data center market is around 55 gigawatts (GW). This is made up of cloud computing workloads (54%), traditional workloads such as email or data storage (32%) and AI (14%) [1].

For the future, analysts predict that electricity demand will increase to 84 GW by 2027. The share of AI is expected to grow to 27%, while the cloud share will fall to 50% and traditional workloads to 23% [1].

By the end of 2030, around 122 gigawatts (GW) of data center capacity will be online.

At this point, I asked myself as a layman how the units mentioned so far are to be understood, in my first graphic I speak of 1,000 TWh of energy consumption of all data centers by 2030 and now here is talk of 122 GW of data center capacity? In order not to go completely beyond the scope of the article, I have added a section at the very end in case some of you also feel like a layman and want to put the "units" into perspective.

... and now on with the article...

One central problem remains:
Where does all the energy come from?

⚡️Energieversorgung: Can the grid keep up?

According to estimates by Goldman Sachs, more than 720 billion US dollars will have to be invested in expanding the power grid worldwide by 2030 in order to supply the new data centers with sufficient energy [1].

Europe in particular, where electricity consumption was expected to decline for many years, is experiencing a veritable "demand shock" [1].

Which energy sources supply data centers?

• Natural gas & battery storage:

Natural gas is seen as a realistic short-term solution to meet continuous demand. It serves as a bridging technology until renewable energy and storage solutions are further developed, as renewable energy is not available around the clock [4].

• Renewable energies:

Wind and solar energy could cover around 80 % of demand in the long term, provided that sufficient storage solutions are integrated [4].

In practice, solar plants run on average only about 6 hours per day, while wind power plants run on average 9 hours per day. There is also a daily volatility in the capacity of these sources, depending on the radiation of the sun and the strength of the wind [4].

The graph shows the fluctuations in capacity factors for wind and solar energy in the USA in 2023. The capacity factor indicates how efficiently an energy source utilizes its maximum output throughout the year.

• Wind energy (light blue line): The highest capacity factors occur in the winter months (Jan-March) and drop significantly in the summer months (Jun-Aug).

• Solar energy (dark blue line): Efficiency rises in the spring (Mar-May) and reaches its maximum in the summer months (Jun-Aug) before falling in the winter (Nov-Dec).

The graph illustrates that wind and solar energy can complement each other seasonally: While wind is more efficient in winter, solar energy provides the highest yields in summer. This shows how important a balanced energy mix is to ensure security of supply.

In addition to finding environmentally friendly energy sources to power data centers, technology providers can reduce emissions intensity through efficiency gains.

The following chart shows the development of the workload and energy consumption of data centers between 2015 and 2023. Although the workload almost tripled, energy consumption remained almost constant until 2019 thanks to efficiency gains. The efficiency gains then slowed down from 2020 onwards.

Source: [4], primary: Masanet et al. (2020), IEA, Cisco, Goldman Sachs Research

This chart supports the discussion on the Jevons paradox (see below). Efficiency gains could be offset or even exceeded in the long term by higher workloads and AI demand. This highlights the need to make data center energy sources more sustainable.

• Nuclear energy:

Meanwhile, governments are also becoming more supportive of nuclear energy on the whole. Switzerland is reconsidering the use of nuclear generators for its electricity supply, while nuclear energy enjoys bipartisan support in the US and the Australian opposition party has put forward plans to introduce nuclear reactors [4].

Participants at the COP28 conference at the end of 2023, an annual summit convened by the United Nations to combat climate change, agreed to triple global nuclear capacity by 2050 [4].

Nuclear energy is considered the ideal option for basic power supply as it provides a reliable and constant supply of energy.

As a result, more and more large tech companies such as Alphabet, Amazon and Microsoft are turning to small modular nuclear power plants (SMRs).

📊 Increasing efficiency & the Jevons paradox

With new technologies such as DeepSeek, AI could work more efficiently in the future. But does greater efficiency automatically mean that less computing power is required?

The Jevons paradox: More efficiency = more consumption?

The Jevons paradox describes the fact that increases in efficiency often do not lead to lower consumption, but to higher consumption overall.

-Example:

In the 19th century, more efficient steam engines did not lead to lower coal consumption; on the contrary, as the machines became cheaper and more versatile, coal consumption actually increased.

With cars: more fuel-efficient engines did not lead to less gasoline consumption, but to people driving more cars.

-Applied to AI:

As AI models become more efficient, the cost per computation decreases. This makes AI applications attractive in even more areas, which in turn leads to a higher overall demand for computing power.

🌎 Regional distribution and global expansion of data center infrastructure

Current distribution: Where are the data centers located today?

Today, most data centers are located in the Asia-Pacific region and North America. Well-known locations are:

North America:

- Northern Virginia

- San Francisco Bay Area

Asia: Beijing

- Beijing

- Shanghai

These regions are characterized by high computing power, intensive data traffic and strong demand from corporate campuses [1].

The chart also shows the historical development of data center capacity by region (North America, APAC, etc.) from 2017 to 2024. The figures illustrate how fast the infrastructure for the AI revolution is growing and underlines why the energy requirements of data centers are increasing so rapidly.

The increase in capacity from around 20 GW in 2017 to almost 60 GW in 2024 shows an enormous growth trend. This correlates directly with the increasing demand for AI applications and cloud computing.

How is supply growing?

Goldman Sachs Research estimates that global data center capacity will increase to around 122 GW by the end of 2030, as mentioned above. The share of hyperscalers and specialized operators will increase from the current 60% to around 70% [1].

• Asia-Pacific:

The largest expansion of data centers has been recorded here in the past ten years.

• North America:

The largest expansion of new data centers is planned in North America over the next five years.

📈 Investment opportunities: Some winners of the AI and data center revolution

US shares e.g.:

• Carrier Global $CARR (-4.49%) : Precise cooling technology and air conditioning for data centers
• Vertiv Holdings $VRT (-11.98%) : Specialist in cooling and power solutions specifically for data centers
• Brookfield Renewable Partners $BEP.UN : Leading provider of renewable energy (hydropower, solar, wind) - supply contracts (PPAs) with data centers
• ON Semiconductor $ON (-4.14%) : Leader in chips for energy efficiency and thermal management. Solutions reduce power consumption in data centers and support AI integration
• Texas Instrumentes $TXN (-7.04%) : Energy-saving semiconductor products used in data center servers
• Equinix $EQIX (-3.33%) : Specialized in data center infrastructure
• Digital Realty $DLR (+0.82%) : Provider of physical infrastructure for data centers
• IBM $IBM (-5.4%) : Quantum computing technologies that potentially consume less energy and development of energy-efficient AI solutions
• Arista Networks $ANET (-9.36%) : Specialist in high-speed networking products for data centers
• Nvidia $NVDA (-7.25%) : Leader in AI GPUs, Leader in AI training market. Best choice for large AI models and data center training
• AMD $AMD (-8.01%) Competing with Nvidia with its own AI chips, but better positioned in the AI interference market where energy efficiency and cost-effectiveness are key. The interference market will be the next most important market, perhaps even the more important one.
• Broadcom $AVGO (-5.25%) Profits from network solutions for data centers
• Microsoft $MSFT (-2.36%) Google $GOOGL (-2.74%) Amazon $AMZN (-3.47%) : The big hyperscalers investing heavily in AI and cloud

European stocks e.g.:

• Siemens Energy $ENR (-9.65%) Important role in modernizing power grids, integrating renewable energies and improving storage solutions for data center reliability
• Schneider Electric $SU (-8.15%) : Leader in the development of energy management and cooling technology for data centers - specialty in the automation of both systems.
• ASML $ASML (-2.09%) : Indispensable for modern chip production
• Infineon $IFX (-8.33%) and STMicroelectronics $STM (-5.31%) : Leading semiconductor companies with a focus on AI applications
• RWE $RWE (-6.04%) and Enel $ENEL (-5.59%) Utilities that are increasingly focusing on renewable energies for data centers

Japanese stocks e.g:

• Daikin Industries $6367 (-1.2%) World market leader in air conditioning and cooling, offers specialized cooling systems for data centers and AI-supported plant management systems to further increase efficiency
• Tokyo Electron $8035 (-6.71%) : Important supplier for semiconductor manufacturing
• Mitsubishi Heavy Industries $7011 (-8.4%) : Works on the development of new nuclear power plants to secure the energy supply

🧠 Conclusion: AI, data centers & energy as the trend of the century?

Although some analysts warn of possible overinvestment, the figures indicate that the demand for computing power and energy for AI data centers will continue to rise sharply.

• Efficiency gains from models such as DeepSeek or new chip technologies could reduce energy consumption per computer, but the Jevons paradox means that overall demand will increase because more efficient systems will be used more often.

The biggest winners are therefore:

• Semiconductor companies: They supply the AI chips needed.
• Data center operators: They build the necessary infrastructure.
• Energy suppliers: They ensure the energy supply for the AI revolution.

In the long term, these companies could be among the biggest beneficiaries of the coming decades.

👨🏽‍💻 How do I position myself?

Personally, I think I am well positioned with the NASDAQ 100 $CSNDX (-5.5%) (portfolio share of 23%), as the focus is on US technology and growth stocks. The ETF complements my All-World with a stronger weighting in innovative sectors such as AI and cloud computing.

In the near future, I will also take a closer look at Daikin Industrie $6367 (-1.2%) share in order to increase the exposure to Japan and the share price offers an entry point at first glance.

In addition, AMD $AMD (-8.01%) has also caught my attention, the reason being its positioning in the aforementioned interference market. Most of the capital is currently flowing into the expansion of new AI models. However, as soon as these become a "commodity" and everyone uses them, most of the capital will probably flow into the interference market (market for the application of AI models).

Furthermore, I have Siemens AG $SIE (-7.45%) with approx. 2.3% portfolio share (still growing to approx. 4%), which I also see as well positioned for the future for the following reasons (in the context of the article):

Network stability

• Develops technologies for intelligent power grids ("smart grids"), essential for integrating renewable energies into the supply of data centers.

Data center control

• Provides automation and monitoring systems that optimize the energy consumption and efficiency of data centers

Efficient building structure

• The "Smart Infrastructure" division supports data centers with energy-efficient solutions for lighting, air conditioning and building monitoring

Not directly cooling systems, but:

• offers technologies that increase the energy efficiency of cooling systems by optimizing energy flows and data analysis

What is your opinion❓

• Which companies do you have on your radar?
• Is there a threat of overinvestment or are we just at the beginning of a century revolution?

Thanks for reading! 🤝

...Said digression follows after the sources...

__________

Sources:

[1] https://www.goldmansachs.com/insights/articles/ai-to-drive-165-increase-in-data-center-power-demand-by-2030

[2] "The Coal Question"
http://digamo.free.fr/peart96.pdf

[3] https://de.m.wikipedia.org/wiki/Jevons-Paradoxon

[4] https://www.goldmansachs.com/insights/articles/is-nuclear-energy-the-answer-to-ai-data-centers-power-consumption

[5] https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

[6] https://www.cbre.com/insights/reports/global-data-center-trends-2024

__________

🧭 Digression: on gigawatts and terawatt hours

In order to understand the relationship between the two figures, 122 GW (gigawatts) and 1,000 TWh (terawatt hours), it is important to clarify the units and their meaning:

• 122 GW (gigawatts):

Refers to the current average power capacity that data centers worldwide require to function. Power (measured in GW) describes the amount of energy consumed per second. This is therefore a snapshot of energy requirements.

• 1,000 TWh (terawatt hours):

This is an indication of energy consumption over a certain period of time, in this case one year. It describes how much energy is required in total in 12 months.

The forecast of 1,000 TWh is slightly below the value resulting from the calculation. The graph shows values slightly above 1,000 TWh; according to the calculation based on 122 GW of power capacity, energy consumption should be around 1,069 TWh.

Nevertheless, general reasons for deviations may be as follows:

• Efficiency improvements: Data centers could operate more efficiently through improved cooling, optimized hardware and software and thus consume less energy.
• Peak vs. average consumption: The figure of 122 GW could reflect peak demand, while the actual average annual demand is somewhat lower.
• Adjustments to the model: It is possible that the forecast of 1,000 TWh is conservative and does not take into account all additional loads or regional differences.

This shows how much the demand for data centers and energy will increase due to AI and digitalization by 2030

__________

Redistribution of call money (falling interest rates) in $NVDA (-7.25%) 7k, $TSM (-6.15%) 2k and $8035 (-6.71%) 1k. Hopefully there will be no punitive tariffs 😁🍿

Today, my overweighting of the semiconductor sector is once again falling flat on its face.

Which stocks would you sell and why? Or would you stick with it everywhere?

$NVDA (-7.25%)
$HY9H (-10.55%) . $QCOM (-7.67%) . $MU (-12.45%)
$ASML (-2.09%)
$8035 (-6.71%)

$ASML (-2.09%) increased ✌️nun 2nd largest share position,

but $MC (-4.14%) somewhat reduced and $8035 (-6.71%) sold.

From sand to chip: how is a modern semiconductor made?

Reading time: approx. 10min

1) INTRODUCTION

Since 2023 at the latest and the rapid rise of Nvidia $NVDA (-7.25%) semiconductors and "AI chips" in particular have been the talk of the town. Since then, investors have been chasing after almost every company that has something to do with the manufacture of chips, driving share prices to unimagined heights. However, hardly any investors really know how complex the value chain is within the production of modern chips.

In this article, I will give you an overview of the entire manufacturing process and the companies involved. Even if many of you have a vague idea that the production of modern chips is complex, you will certainly be surprised by how complex it really is in reality.

2) BASIC

The starting point for every chip are so-called wafers [1] - i.e. thin wafers, which usually consist of so-called high-purity monocrystalline silicon. In the field of power semiconductors, which primarily comprises chips for applications with higher currents and voltages, silicon carbide (SiC) or galium nitride (GaN) has recently also been used as the base material for the wafers.

In the so-called front end the actual core components of the chips - the so-called dies - are created and applied to the wafers. The dies are rectangular structures that contain the actual functionality of the later chip. The finished dies are then tested for their functionality and electrical properties. Each die that is found to be good is then integrated into the so-called backend where the individual dies are separated on the wafer. This is followed by the so-called packaging. The individual dies from the front end are then electrically contacted and integrated into a protective housing. In the end, this housing with the contacted die is what is usually called a chip chip.

Now that we have a rough overview of the overall process, let's take a closer look at the individual processes involved in producing the dies on the wafer. This is the area in which most highly complex machines are used and which is usually the most sensitive.

3) FROM SAND TO WAFER

Before wafers made of high-purity silicon can even be produced and the actual process for manufacturing dies can begin, the actual wafer must first be manufactured in almost perfect quality. To do this, quartz sand, which consists largely of silicon dioxide, is reduced with carbon at high temperatures. This produces so-called raw siliconwhich, with a purity of around 96%, is not yet anywhere near the quality required for the production of wafers.

In several chemical processes, which are carried out by Wacker Chemie
$WCH (-7.57%) or Siltronic
$WAF (-7.01%) are used to turn the "impure" silicon into so-called polycrystalline silicon with a purity of 99.9999999%. For every billion silicon atoms, there is then only one foreign atom in the silicon. However, this pure polycrystalline silicon is still not suitable for the production of wafers, as the crystal structure in the silicon is not uniform enough. In order to create the right crystal structure, the polycrystalline silicon is then melted again and a so-called ingotwhich is made from monocrystalline silicon is produced. A comparison between raw silicon and the ingot can be found in the following image [3]:

This ingot is then sawn into thin slices, which are then the final wafers for semiconductor production. The best-known wafer producers are Shin Etsu
$4063, (-7.17%)
Siltronic or GlobalWafers
$6488.

4) FROM THE WAFER TO THE DIE

The wafers described in the previous section can now be used to produce dies. The overall process for producing dies basically consists of applying a large number of layers using various chemical, mechanical and physical processes. The overall process (depending on the product) takes approx. 80 different layers on the wafer, requiring almost 1000 different process steps and 3 months
non-stop production are required [4].

A macroscopic analogy is useful here, which I have also taken from [4]. You can compare the overall process for producing dies with baking a large multi-layer cake. This cake has 80 layers and the recipe for baking consists of 1000 steps. It takes 3 months to make the cake and if even one layer of the cake deviates from the recipe by more than 1%, the whole cake collapses and has to be thrown away.

In the first process steps, the wafer is covered with billions tiny little transistors are created on the wafer, which are then all individually electrically contacted in the following steps. The final steps consist of electrically connecting the transistors to each other, resulting in a complete electrical circuit [4]:

Each individual layer of the approximately 80 layers in the die requires highly specialized processes, which can be roughly summarized as:

• Applying masks: Photolithography, photoresist coating (applying photoresist)
• Apply material: Chemical Vapor Deposition (CVD), Physical Vapor Deposition (PVD), Atomic Layer Deposition
• Remove material: Plasma annealing, Wet annealing, Chemical Mechanical Planarization (CMP)
• Modify material: Ion Implanting, Annealing
• Material cleaning
• Inspecting the layers: Optical, Microscopical, Focused Ion Beam, Defect Inspection

Apply masks

Ultimately, a mask can be thought of as an enlarged copy of the structure of a special layer in the die. These so-called photomasks are then scanned using so-called scanners or steppers "copied" in reduced size onto the wafer. The best-known manufacturer of such lithography systems is ASML
$ASML (-2.09%). It is currently the only producer of lithography systems that make it possible to produce structures smaller than 10 nanometers on the wafer. In today's powerful and modern chips, such as those found in smartphones, AI chips and processors, the smallest structures are around 3 nanometers in size. Other manufacturers of lithography systems for larger structures (10nm and larger) are Canon Electronics
$7739 or Nikon $7731 (-6.32%) .

The photomasks - i.e. the enlarged "copies" of the structures - are produced by companies such as Toppan $7911 (-5.81%) , Dai Nippon Printing
$7912 (-4%) or Hoya $7741 (-2.68%) manufactured. Systems for cleaning the photomasks or for applying the photoresist are produced, for example, by Suss Microtec
$SMHN (-4.06%) for example.

Apply/remove/modify/clean material

As can be seen in the overview above, there are a variety of methods and processes for modifying the material of a particular layer. As a result, there is a lot of different equipment that can handle a process very well with incredible specialization. The best-known and most successful equipment manufacturers include Applied Materials $AMAT (-5.46%), LAM Research
$LRCX (-8.64%), Tokyo Electron (TEL)
$8035, (-6.71%)
Suss Mictrotec, Entegris
$ENTG (-8.38%) and Axcelis $ACLS (-4.64%).

The material - for example, highly specialized chemicals - is of course also required for production. Companies such as Linde
$LIN (-5.56%), Air Liquide
$AI (-0.98%), Air Products
$APD (-6.2%) and Nippon Sanso
$4091 (-2.44%) are major manufacturers of process gases such as nitrogen, hydrogen and argon.

Inspect

As mentioned, every single layer in the manufacturing process of a die must be perfect in order to obtain a functional die at the end. Any small deviation or foreign particles can impair the functionality of the die. As the function of the die can only be checked precisely on the finished die, it is advantageous to inspect the individual layers for defects and deviations during production. Special machines are required for this, which must be able to do different things depending on the layer. Manufacturers of such machines include KLA
$KLAC (-6.34%) or Onto Innovation
$ONTO (-4.03%).

The following applies to almost all of the companies mentioned in this section: the companies are highly specialized and have quasi-monopolies on the machines for certain process steps. quasi-monopolies. Suitable equipment therefore usually costs several million dollars. In addition, some of the systems are so complex that they can only be serviced by the manufacturer's own service staff, which results in recurring service revenues for every machine sold. As a rule, each machine requires several highly specialized engineers to ensure long-term stable operation.

5) FROM THE DIE TO THE FINISHED CHIP

Once the wafer has been processed, the dies on the wafer are checked for functionality. There is highly specialized equipment for this, so-called probers. These probers test each individual chip several times, if necessary, to check the functionality implemented in the design. Manufacturers of such probers include Teradyne $TER (-7.73%), Keysight Technologies
$KEYS (-5.64%), Onto Innovation or Tokyo Electron. These probers have to control each individual die, some of which are only a few square millimetres in size, and contact the corresponding much smaller test structures with tiny needles. The testing process is sometimes outsourced to entire companies that offer die testing as a complete package. One example of such providers is Amkor Technology
$AMKR (-4.89%).

The processed and tested wafer is now sawn to obtain individual dies. The dies that are found to be good are then integrated into a protective housing in the backend. The dies that have not passed the functionality test are either sorted out or (depending on the error pattern) processed as a variant with reduced functionality similar to those with full functionality. After a final functional test in the package, the chip is ready for use.

6) FOUNDRIES, FABLESS & SOFTWARE

Now that we have an overview of the complex process of manufacturing a chip, let's zoom out a little further to understand which companies perform which tasks in the semiconductor industry.

It's funny that not once in the manufacturing process has the name Nvidia $NVDA (-7.25%) or Apple $AAPL (-6.8%) has been mentioned? Yet they have the most advanced chips, don't they?

The pure production of the chips is done by other companies - so-called foundries. Companies like Nvidia and even AMD $AMD (-8.01%) are in fact fablessThis means that they do not have their own production facilities but only supply the chip design and let the foundries manufacture the actual chip according to their design.

The design of a chip is like the blueprint for production - the foundries then take over the recipe creation and the actual production. There is special software for designing chips. Companies known for this software include Cadance Design
$CDNS (-6.34%) and Synopsys $SNPS (-6%). But also the industrial giant Siemens
$SIE (-7.45%) now also supplies software for designing integrated circuits. Synopsys also offers other software for data analysis within foundry production.

Speaking of foundries; the best known foundry is probably TSMC
$TSM, (-6.15%) which is the global market leader in foundries. TSMC designs itself no chips itself and specializes exclusively in the production of the most advanced generations of chips. Another major player that also masters the most advanced structure sizes is Samsung $005930. In contrast to TSMC manufactures Samsung also produces its own designs. Other large foundries are Global Foundries
$GFS, (-7.62%) which was originally a spin-off from AMD and the Taiwanese company United Micro Electronics
$UMC. (-2.14%)

The best-known fabless companies - i.e. companies without their own chip production - are Nvidia, Apple, AMD, ARM Holdings
$ARM, (-9.86%)
Broadcom $AVGO (-5.25%), MediaTek $2454 and Qualcomm $QCOM. (-7.67%) In the meantime Alphabet $GOOGL, (-2.74%)
Microsoft $MSFT, (-2.36%)
Amazon $AMZN (-3.47%) and Meta $META (-4.44%) have designed their own chips for certain functionalities and then have them manufactured in foundries.

In addition to foundries and fabless companies, there are of course also hybrid models, i.e. companies that take on both production and design. The best-known examples of this are, of course, companies such as Intel
$INTC (-10.9%) and Samsung. There is also a whole range of so-called Integrated Device Manufacturer (IDM)which for the most part only manufacture their own designed chips and do not accept customer orders for production. Well-known companies such as Texas Instruments
$TXN, (-7.04%)
SK Hynix
$000660,
STMicroelectronics
$STMPA, (-4.97%)
NXP Semiconductors
$NXPI, (-6.1%)
Infineon $IFX (-8.33%) and Renesas $6723 (-15.64%) are among the IDMs.

FINAL WORD

The aim of this article was to provide an overview of the complexity of the semiconductor industry. I do not, of course no claim to be complete, as there are of course many other companies that are part of this value chain. As Getquin thrives on active exchange, I'll give you some food for thought to discuss in the comments below the article:

• feel free to link any other companies in the comments if you think I've forgotten any relevant ones
• what was the most surprising new information for you from the article?
• which companies from the article have you never heard of?
• before reading the article, did you know approximately how a modern chip is produced and what steps are necessary for this?

In general, I can recommend the 20-minute YouTube video at [4] to any interested reader. It provides an excellent animated overview of the manufacturing process of modern chips.

Stay tuned,

Yours Nico Uhlig (aka RealMichaelScott)

SOURCES:

[1] Wikipedia: https://de.wikipedia.org/wiki/Wafer

[2] https://www.halbleiter.org/waferherstellung/einkristall/

[3] https://solarmuseum.org/wp-content/uploads/2019/05/solarmuseum_org-07917.jpg

[4] Branch Education on YouTube: "How are Microchips Made?" https://youtu.be/dX9CGRZwD-w?si=xeV0TYgJ2iwNOKyO

Profit taking:

Reallocation of 40% into $VWCE (-5.52%) , 10% $IGLN (-2.39%) and 50% in individual stocks $8035 (-6.71%) , $GOOG (-2.49%) , $ASML (-2.09%) , $MSFT (-2.36%)

Having Charlie Munger in your profile picture doesn't mean you're as successful as him.

A look at the end of my portfolio shows the weaknesses and mistakes that were made.

You might say Fallen Angel.

LVMH $MC (-4.14%)

Bought on 30.05.2023 after bad news and a fall in the share price.

In the back of my mind sayings like

"Luxury always works" and "LVMH is a long runner"

Suddenly, every influencer at the time had

had the share on their buy list and so on

28.09.2023 was bought again.

However, there was no shortage of bad news, the share disappointed and an exit was missed.

Tokyo Electron $8035 (-6.71%)

Due to US sanctions against China, there were considerations that China would obtain high technology from Japan.

And due to the AI hype, on 8.02.2024

Tokyo Electron and Advantest $6857 (-9.74%) were bought at a high P/E ratio.

Nevertheless, Tokyo Electron was quickly up over 30% and the joy was great. And a stop loss was not set either.

And so it was that an interest rate hike in Japan caused the stock market to crash. And of course the first to be hit were the overvalued hype stocks.

Incidentally, Advantest is already well up again after good figures and I am optimistic about Tokyo and fortunately the position is only very small.

ASML $ASML (-2.09%)

History repeats itself. Here, too, we bought after the market correction on 31.07.2024.

Again it was influencers "ASML has a monopoly"

and it won't get any cheaper.

But things turned out differently. The last quarterly figures proved me wrong.

This now raises the question for me

BUY UP ?

or will it be an LVMH story?

My dears,

Of course I am aware that in the long term I will be smiling about this in a few years' time.

And a diversified portfolio keeps such things in check.

But as a clever investor, I could have invested LVMH's money better over a year.

Please tell me your opinion on this, do you set stop losses and where do you set them?

What strategy do you use?

When do you buy?

etc.

You are the best community, and I would especially like to mention that the tone has changed for the better in the last few weeks.

Thank you my dears 😘🌹