For many years, semiconductor technology progress was largely described by process nodes.
10 nm became 7 nm, then 5 nm, 3 nm and increasingly advanced transistor architectures.
But as AI accelerators, high-performance computing systems and advanced packaging continue to scale, transistor density is no longer the only factor determining system performance.
The semiconductor industry is entering a new stage in which materials, packaging, interconnects, power delivery and thermal management are becoming just as important as transistor scaling.
This transition is increasing interest in a wide range of semiconductor materials, including:
These materials address different engineering challenges, but they are increasingly connected by one common driver:
AI and HPC systems require more bandwidth, higher power efficiency, denser packaging and better thermal performance.
![]()
Modern AI processors contain enormous computing resources.
However, increasing computing capability also creates several new limitations.
Two of the most important are:
data movement
and
heat removal.
An AI accelerator must continuously exchange enormous amounts of data with HBM, neighboring processors, network interfaces and storage systems.
At the same time, these devices generate increasingly concentrated heat inside advanced packages.
As a result, improving transistor performance alone is no longer sufficient.
The entire semiconductor system must be optimized.
That includes:
This system-level transition is creating new opportunities for semiconductor materials that previously occupied relatively specialized markets.
Silicon-on-insulator is not a new semiconductor material.
SOI wafers have long been used for applications such as:
However, AI infrastructure is creating another important growth opportunity:
silicon photonics.
As AI clusters become larger, traditional electrical interconnects increasingly face challenges related to:
Copper interconnects remain essential, but optical communication becomes increasingly attractive as bandwidth requirements rise.
This is one reason co-packaged optics, or CPO, has received so much attention.
Instead of relying entirely on electrical connections between high-performance processors and optical modules, CPO brings optical engines closer to the switching or computing silicon.
The objective is to reduce the length of high-speed electrical connections and transfer more data optically.
SOI is one of the most important substrate platforms for silicon photonics.
A typical photonics SOI wafer contains:
The top silicon layer can be patterned into optical waveguides.
The buried oxide provides optical isolation between the silicon waveguide and the underlying silicon substrate.
Common silicon photonics structures include:
In many commercial photonics platforms, the silicon device layer is only a few hundred nanometers thick.
For example, approximately 220 nm silicon on a micrometer-scale buried oxide is widely used in silicon photonics.
This means that SOI wafer requirements for photonics are very different from those for MEMS or conventional RF applications.
Thickness uniformity, surface quality and BOX control become particularly important.
The importance of silicon photonics is closely connected to the growth of AI data centers.
AI clusters require extremely high-speed communication:
inside the server
between accelerators
between racks
and increasingly
between large groups of computing systems.
As electrical communication approaches practical limits in power and bandwidth density, optical interconnect technologies are becoming more important.
This means SOI may increasingly serve not only traditional semiconductor devices but also the optical communication infrastructure surrounding AI processors.
In other words:
The AI boom is not only increasing demand for compute wafers. It is also increasing demand for the materials that move data between compute devices.
Another material receiving increasing attention is glass.
Glass has been used in electronics and semiconductor manufacturing for many years.
Applications include:
But advanced packaging is creating a new opportunity:
glass core substrates and glass interposers.
Modern AI packages may combine:
As package dimensions increase, dimensional stability becomes increasingly important.
Traditional organic substrate materials can face challenges including:
Glass offers several attractive characteristics for these applications.
Glass can provide:
One of the most important enabling technologies is:
TGV — Through Glass Via.
A TGV creates electrical connections through the thickness of a glass substrate.
After via formation, the holes can be metallized to create vertical electrical interconnects.
This makes glass potentially suitable for:
However, glass packaging requires more than simply producing a glass wafer.
The full manufacturing chain can include:
glass preparation
→ precision thinning
→ TGV formation
→ metallization
→ redistribution layers
→ bonding
→ advanced packaging
The companies that can control the complete process chain may capture more value than suppliers of raw glass alone.
High Bandwidth Memory has become one of the defining technologies of AI hardware.
HBM improves memory bandwidth by stacking multiple DRAM dies vertically.
This allows much more data to move between memory and processors than with conventional memory architectures.
But there is an unavoidable consequence:
stacking makes thermal management more difficult.
Multiple silicon dies generate heat inside a compact vertical structure.
At the same time, HBM stacks are typically placed very close to high-power AI accelerators.
An advanced AI package may therefore contain several major thermal sources in a limited area.
Thermal performance becomes a package-level design problem.
Traditional copper remains extremely important because of its:
However, future high-power packages may require materials with additional capabilities.
Potential materials include:
The key material parameters increasingly include:
The thermal problem is therefore no longer solved simply by attaching a larger heat sink.
It begins much closer to the semiconductor die.
Diamond is receiving renewed attention because of one exceptional property:
very high thermal conductivity.
High-quality synthetic diamond can conduct heat much more effectively than many conventional thermal-management materials.
This makes diamond attractive for removing concentrated heat from high-power devices.
Potential applications include:
However, pure diamond also has disadvantages.
These include:
As a result, researchers and material suppliers are increasingly exploring hybrid solutions.
One important example is:
Diamond-Copper Composite.
Diamond-copper composites combine diamond particles with a copper matrix.
The goal is to achieve two properties simultaneously:
higher thermal conductivity
and
lower thermal expansion than conventional copper.
This is attractive because semiconductor packaging frequently faces a tradeoff.
Copper conducts heat well but has a relatively high coefficient of thermal expansion.
Low-CTE materials such as tungsten and molybdenum can improve dimensional stability but often reduce thermal conductivity when added to copper.
Diamond offers a different possibility because it has both:
In principle, Diamond/Cu composites can therefore improve thermal conductivity while reducing CTE.
A Diamond-Copper composite is not automatically high performance simply because it contains a large percentage of diamond.
The most difficult issue is the interface between:
diamond
and
copper.
Copper does not naturally bond efficiently with diamond.
Poor interface contact can create substantial thermal resistance.
This means heat cannot move efficiently from:
copper → diamond → copper.
Manufacturers therefore use interface engineering techniques involving carbide-forming elements such as:
These elements can improve diamond-copper bonding.
However, the interface layer must also be carefully controlled.
If it becomes too thick, the interface itself can become a thermal barrier.
This illustrates an important trend in advanced semiconductor materials:
Future performance increasingly depends on interface engineering, not only bulk material properties.
Although AI receives enormous attention, electrification continues to drive another major semiconductor transition:
the expansion of silicon carbide power devices.
SiC has become important for applications including:
Compared with conventional silicon power devices, SiC can enable:
But as the SiC industry matures, the question is changing.
Several years ago, the discussion often focused on:
Can SiC replace silicon in high-power applications?
Today, the more practical question is:
How can SiC manufacturing cost be reduced?
SiC substrates are difficult to manufacture.
Challenges occur throughout the processing chain:
SiC is extremely hard and brittle.
This makes wafer cutting and polishing slower and more expensive than conventional silicon processing.
Substrate defects can also influence final device yield.
As SiC moves toward larger-volume manufacturing, the focus increasingly shifts toward:
This means opportunities exist not only in SiC wafers themselves but throughout the SiC materials and equipment supply chain.
Gallium nitride is another major wide-bandgap semiconductor.
GaN is particularly attractive where:
are important.
AI data centers consume enormous amounts of electricity.
Reducing power conversion loss throughout the server and rack architecture is therefore increasingly valuable.
GaN devices can be used in high-frequency power conversion applications including:
This creates another important connection between AI and semiconductor materials.
AI demand does not only create demand for processors.
It also increases demand for the power electronics required to operate those processors efficiently.
SiC and GaN are often presented as competing semiconductor technologies.
In practice, they frequently address different parts of the power electronics market.
SiC performs particularly well in:
GaN is highly attractive for:
The future power semiconductor market is therefore likely to use:
silicon + SiC + GaN
rather than selecting a single universal material.
Another less visible but increasingly important category is advanced ceramic materials.
Semiconductor equipment relies on precision ceramics for components such as:
Important materials include:
Each material solves a different engineering problem.
Alumina offers:
It remains one of the most widely used technical ceramics.
AlN is especially attractive when both:
electrical insulation
and
high thermal conductivity
are required.
This makes it useful for:
SiC ceramic provides:
These characteristics make it attractive for semiconductor equipment components that must remain dimensionally stable under changing thermal conditions.
One of the most interesting semiconductor trends is that materials that once belonged to relatively separate industries are becoming part of the same system.
Consider an advanced AI computing platform.
It may require:
SOI
for silicon photonics and optical communication.
Glass
for next-generation advanced packaging.
HBM
for high-bandwidth memory.
AlN or SiC ceramics
for thermal management and semiconductor equipment.
Diamond-based materials
for extreme heat spreading.
GaN
for high-efficiency data-center power conversion.
SiC
for high-power energy infrastructure.
These materials perform completely different functions.
But AI is connecting them through the same engineering requirements:
The semiconductor industry is therefore entering a broader form of scaling.
Traditional scaling focused primarily on making transistors smaller.
Future performance improvements increasingly depend on:
**transistor scaling
This is why advanced materials are becoming strategically important.
A faster transistor provides limited benefit if data cannot reach it quickly.
A larger AI accelerator provides limited benefit if it overheats.
More HBM provides limited benefit if packaging density becomes impossible to manage.
Advanced packaging and materials are becoming part of the performance equation.
Several categories deserve particular attention.
Driven by silicon photonics, high-speed optical communication and CPO.
Driven by chiplets, HBM, large-area advanced packaging and TGV technology.
Driven by EVs, industrial power, renewable energy and high-voltage electronics.
Driven by high-frequency power conversion and data-center efficiency.
Driven by power density and advanced semiconductor equipment.
Driven by extreme heat flux in AI, RF and high-power devices.
These materials will not replace silicon.
Instead, they will increasingly surround silicon and enable it to continue scaling at the system level.
The AI semiconductor revolution is becoming a materials revolution.
For decades, the industry's most important question was:
How small can we make the transistor?
That question remains important.
But it is no longer enough.
The next generation of semiconductor systems must also answer:
How quickly can data move?
How closely can multiple dies be integrated?
How efficiently can power be converted?
How quickly can heat be removed?
These questions are increasing the importance of SOI wafers, glass substrates, SiC, GaN, advanced ceramics and diamond-based thermal materials.
The future of semiconductor performance will therefore depend not only on better chips, but on better materials surrounding those chips.
The next phase of competition may be defined less by a single material breakthrough and more by the ability to integrate multiple specialized materials into one high-performance system.
In the AI era, semiconductor innovation is moving beyond simply making chips smaller.
It is increasingly about enabling chips to communicate faster, package more densely, consume power more efficiently and operate at lower temperatures.
For many years, semiconductor technology progress was largely described by process nodes.
10 nm became 7 nm, then 5 nm, 3 nm and increasingly advanced transistor architectures.
But as AI accelerators, high-performance computing systems and advanced packaging continue to scale, transistor density is no longer the only factor determining system performance.
The semiconductor industry is entering a new stage in which materials, packaging, interconnects, power delivery and thermal management are becoming just as important as transistor scaling.
This transition is increasing interest in a wide range of semiconductor materials, including:
These materials address different engineering challenges, but they are increasingly connected by one common driver:
AI and HPC systems require more bandwidth, higher power efficiency, denser packaging and better thermal performance.
![]()
Modern AI processors contain enormous computing resources.
However, increasing computing capability also creates several new limitations.
Two of the most important are:
data movement
and
heat removal.
An AI accelerator must continuously exchange enormous amounts of data with HBM, neighboring processors, network interfaces and storage systems.
At the same time, these devices generate increasingly concentrated heat inside advanced packages.
As a result, improving transistor performance alone is no longer sufficient.
The entire semiconductor system must be optimized.
That includes:
This system-level transition is creating new opportunities for semiconductor materials that previously occupied relatively specialized markets.
Silicon-on-insulator is not a new semiconductor material.
SOI wafers have long been used for applications such as:
However, AI infrastructure is creating another important growth opportunity:
silicon photonics.
As AI clusters become larger, traditional electrical interconnects increasingly face challenges related to:
Copper interconnects remain essential, but optical communication becomes increasingly attractive as bandwidth requirements rise.
This is one reason co-packaged optics, or CPO, has received so much attention.
Instead of relying entirely on electrical connections between high-performance processors and optical modules, CPO brings optical engines closer to the switching or computing silicon.
The objective is to reduce the length of high-speed electrical connections and transfer more data optically.
SOI is one of the most important substrate platforms for silicon photonics.
A typical photonics SOI wafer contains:
The top silicon layer can be patterned into optical waveguides.
The buried oxide provides optical isolation between the silicon waveguide and the underlying silicon substrate.
Common silicon photonics structures include:
In many commercial photonics platforms, the silicon device layer is only a few hundred nanometers thick.
For example, approximately 220 nm silicon on a micrometer-scale buried oxide is widely used in silicon photonics.
This means that SOI wafer requirements for photonics are very different from those for MEMS or conventional RF applications.
Thickness uniformity, surface quality and BOX control become particularly important.
The importance of silicon photonics is closely connected to the growth of AI data centers.
AI clusters require extremely high-speed communication:
inside the server
between accelerators
between racks
and increasingly
between large groups of computing systems.
As electrical communication approaches practical limits in power and bandwidth density, optical interconnect technologies are becoming more important.
This means SOI may increasingly serve not only traditional semiconductor devices but also the optical communication infrastructure surrounding AI processors.
In other words:
The AI boom is not only increasing demand for compute wafers. It is also increasing demand for the materials that move data between compute devices.
Another material receiving increasing attention is glass.
Glass has been used in electronics and semiconductor manufacturing for many years.
Applications include:
But advanced packaging is creating a new opportunity:
glass core substrates and glass interposers.
Modern AI packages may combine:
As package dimensions increase, dimensional stability becomes increasingly important.
Traditional organic substrate materials can face challenges including:
Glass offers several attractive characteristics for these applications.
Glass can provide:
One of the most important enabling technologies is:
TGV — Through Glass Via.
A TGV creates electrical connections through the thickness of a glass substrate.
After via formation, the holes can be metallized to create vertical electrical interconnects.
This makes glass potentially suitable for:
However, glass packaging requires more than simply producing a glass wafer.
The full manufacturing chain can include:
glass preparation
→ precision thinning
→ TGV formation
→ metallization
→ redistribution layers
→ bonding
→ advanced packaging
The companies that can control the complete process chain may capture more value than suppliers of raw glass alone.
High Bandwidth Memory has become one of the defining technologies of AI hardware.
HBM improves memory bandwidth by stacking multiple DRAM dies vertically.
This allows much more data to move between memory and processors than with conventional memory architectures.
But there is an unavoidable consequence:
stacking makes thermal management more difficult.
Multiple silicon dies generate heat inside a compact vertical structure.
At the same time, HBM stacks are typically placed very close to high-power AI accelerators.
An advanced AI package may therefore contain several major thermal sources in a limited area.
Thermal performance becomes a package-level design problem.
Traditional copper remains extremely important because of its:
However, future high-power packages may require materials with additional capabilities.
Potential materials include:
The key material parameters increasingly include:
The thermal problem is therefore no longer solved simply by attaching a larger heat sink.
It begins much closer to the semiconductor die.
Diamond is receiving renewed attention because of one exceptional property:
very high thermal conductivity.
High-quality synthetic diamond can conduct heat much more effectively than many conventional thermal-management materials.
This makes diamond attractive for removing concentrated heat from high-power devices.
Potential applications include:
However, pure diamond also has disadvantages.
These include:
As a result, researchers and material suppliers are increasingly exploring hybrid solutions.
One important example is:
Diamond-Copper Composite.
Diamond-copper composites combine diamond particles with a copper matrix.
The goal is to achieve two properties simultaneously:
higher thermal conductivity
and
lower thermal expansion than conventional copper.
This is attractive because semiconductor packaging frequently faces a tradeoff.
Copper conducts heat well but has a relatively high coefficient of thermal expansion.
Low-CTE materials such as tungsten and molybdenum can improve dimensional stability but often reduce thermal conductivity when added to copper.
Diamond offers a different possibility because it has both:
In principle, Diamond/Cu composites can therefore improve thermal conductivity while reducing CTE.
A Diamond-Copper composite is not automatically high performance simply because it contains a large percentage of diamond.
The most difficult issue is the interface between:
diamond
and
copper.
Copper does not naturally bond efficiently with diamond.
Poor interface contact can create substantial thermal resistance.
This means heat cannot move efficiently from:
copper → diamond → copper.
Manufacturers therefore use interface engineering techniques involving carbide-forming elements such as:
These elements can improve diamond-copper bonding.
However, the interface layer must also be carefully controlled.
If it becomes too thick, the interface itself can become a thermal barrier.
This illustrates an important trend in advanced semiconductor materials:
Future performance increasingly depends on interface engineering, not only bulk material properties.
Although AI receives enormous attention, electrification continues to drive another major semiconductor transition:
the expansion of silicon carbide power devices.
SiC has become important for applications including:
Compared with conventional silicon power devices, SiC can enable:
But as the SiC industry matures, the question is changing.
Several years ago, the discussion often focused on:
Can SiC replace silicon in high-power applications?
Today, the more practical question is:
How can SiC manufacturing cost be reduced?
SiC substrates are difficult to manufacture.
Challenges occur throughout the processing chain:
SiC is extremely hard and brittle.
This makes wafer cutting and polishing slower and more expensive than conventional silicon processing.
Substrate defects can also influence final device yield.
As SiC moves toward larger-volume manufacturing, the focus increasingly shifts toward:
This means opportunities exist not only in SiC wafers themselves but throughout the SiC materials and equipment supply chain.
Gallium nitride is another major wide-bandgap semiconductor.
GaN is particularly attractive where:
are important.
AI data centers consume enormous amounts of electricity.
Reducing power conversion loss throughout the server and rack architecture is therefore increasingly valuable.
GaN devices can be used in high-frequency power conversion applications including:
This creates another important connection between AI and semiconductor materials.
AI demand does not only create demand for processors.
It also increases demand for the power electronics required to operate those processors efficiently.
SiC and GaN are often presented as competing semiconductor technologies.
In practice, they frequently address different parts of the power electronics market.
SiC performs particularly well in:
GaN is highly attractive for:
The future power semiconductor market is therefore likely to use:
silicon + SiC + GaN
rather than selecting a single universal material.
Another less visible but increasingly important category is advanced ceramic materials.
Semiconductor equipment relies on precision ceramics for components such as:
Important materials include:
Each material solves a different engineering problem.
Alumina offers:
It remains one of the most widely used technical ceramics.
AlN is especially attractive when both:
electrical insulation
and
high thermal conductivity
are required.
This makes it useful for:
SiC ceramic provides:
These characteristics make it attractive for semiconductor equipment components that must remain dimensionally stable under changing thermal conditions.
One of the most interesting semiconductor trends is that materials that once belonged to relatively separate industries are becoming part of the same system.
Consider an advanced AI computing platform.
It may require:
SOI
for silicon photonics and optical communication.
Glass
for next-generation advanced packaging.
HBM
for high-bandwidth memory.
AlN or SiC ceramics
for thermal management and semiconductor equipment.
Diamond-based materials
for extreme heat spreading.
GaN
for high-efficiency data-center power conversion.
SiC
for high-power energy infrastructure.
These materials perform completely different functions.
But AI is connecting them through the same engineering requirements:
The semiconductor industry is therefore entering a broader form of scaling.
Traditional scaling focused primarily on making transistors smaller.
Future performance improvements increasingly depend on:
**transistor scaling
This is why advanced materials are becoming strategically important.
A faster transistor provides limited benefit if data cannot reach it quickly.
A larger AI accelerator provides limited benefit if it overheats.
More HBM provides limited benefit if packaging density becomes impossible to manage.
Advanced packaging and materials are becoming part of the performance equation.
Several categories deserve particular attention.
Driven by silicon photonics, high-speed optical communication and CPO.
Driven by chiplets, HBM, large-area advanced packaging and TGV technology.
Driven by EVs, industrial power, renewable energy and high-voltage electronics.
Driven by high-frequency power conversion and data-center efficiency.
Driven by power density and advanced semiconductor equipment.
Driven by extreme heat flux in AI, RF and high-power devices.
These materials will not replace silicon.
Instead, they will increasingly surround silicon and enable it to continue scaling at the system level.
The AI semiconductor revolution is becoming a materials revolution.
For decades, the industry's most important question was:
How small can we make the transistor?
That question remains important.
But it is no longer enough.
The next generation of semiconductor systems must also answer:
How quickly can data move?
How closely can multiple dies be integrated?
How efficiently can power be converted?
How quickly can heat be removed?
These questions are increasing the importance of SOI wafers, glass substrates, SiC, GaN, advanced ceramics and diamond-based thermal materials.
The future of semiconductor performance will therefore depend not only on better chips, but on better materials surrounding those chips.
The next phase of competition may be defined less by a single material breakthrough and more by the ability to integrate multiple specialized materials into one high-performance system.
In the AI era, semiconductor innovation is moving beyond simply making chips smaller.
It is increasingly about enabling chips to communicate faster, package more densely, consume power more efficiently and operate at lower temperatures.