Outlook Supplement - Flipbook - Page 12
Forecast
In 2026, managing an expanded and
complex global supply chain will be accomplished with AI-driven collaboration.
It’s important to note that the semiconductor industry has always thrived
on collaboration and innovation. That
has not changed, though the definition of collaboration has expanded
from just referring to partnerships.
It now reflects the ability to leverage
AI at all levels of the ecosystem for
real-time data sharing and multi-party
cooperation, securing data networks,
improving operational efficiency
and cost and increasing supply chain
resiliency.
Challenges noted above will continue to be resolved in 2026 by fundamentally reimagining the current
reactionary model into streamlined
AI-based, mission-critical platforms
sharing data, orchestrating operations
and deploying intelligence across
increasingly complex global supply
chains.
Growing design and manufacturing
costs throughout the semiconductor
supply chain necessitate the transition
to the infrastructure of the future and
the need for analytics to drive efficiency. Starting in 2026, the industry
will begin to see the results of AIdriven collaboration to enable smart
decision making, bridging hundreds
of sites and organizational systems
across the globe, enabling seamless
and protected information sharing.
Packaging and Interconnects
in the Spotlight
STEVE WOO, d i s t i n g u i s h e d
inventor and fellow at
Rambus
As compute gets faster each generation, the industry is fighting an uphill
battle as package sizes grow and
greater numbers of chiplets and HBM
stacks and other memories are integrated into larger super chips. Packaging and IOs will further entrench
themselves as important enabling
10
technologies and
key citizens in chip
and system architecture designs.
Performance,
power-efficiency
and reliability for
STEVE WOO
I/O designs and
pin-field engineering will grow in importance as
data movement requirements dictate
bandwidth needs, which in turn impact
physical design constraints, power
delivery, and thermal management.
Memory supply bottlenecks inside
and outside the data center
Data center investments continue to
rise with AI, and in particular agentic
AI. Greater demand will put pressure
on supply chains as data centers
consume more memory. Non-data
center markets will face headwinds in
memory supply as a result.
Memory will be in focus as applications consume higher capacities and
bandwidths, driving demand for newer
DRAM technologies like HBM4 and
GDDR7 as they ramp. Higher capacity
and bandwidth memory technologies
like stacking, multi-PAM signaling,
and CXL-attached pools will see
growing interest and adoption to keep
accelerators and other computing
infrastructure fully fed with data.
Inference to get
dramatically cheaper
Inference gets dramatically cheaper and
more capable, enabling agentic AI to
move into edge systems where designers must balance performance with
robust security.
• Advances in Specialized Silicon — Purpose-built accelerators
and optimized architectures for
AI inference are reducing cost per
operation, making high-performance inference increasingly viable
outside of hyperscale data centers.
• Energy Efficiency Gains — Im-
| Supplement to January 2026 Semiconductor Digest
provements in power consumption
per inference task means edge
devices can run more sophisticated
models without prohibitive thermal
or battery constraints.
• Economies of Scale and Process
Shrinks — Mature 3nm and emerging 2nm nodes, combined with chiplet-based designs, will drive down
costs while maintaining application
performance targets.
On-device AI becomes a
default design choice (“mixed
compute” is the new cloud)
By 2026, most premium PCs and
phones will ship with NPUs at approx.
40–45 TOPS and OS features that
include local inference for speed,
privacy, and cost control—while
bursting to the cloud for heavier jobs.
Microsoft’s Copilot+ PC program
(Windows 11, NPUfirst features) and
Qualcomm’s Snapdragon X platforms
demonstrate the hardware baseline;
Apple’s Apple Intelligence roadmap
expands ondevice LLMs, Live
Translation, visual intelligence and
developer access to the local foundation model.
Expect privacycentric, batteryefficient experiences (search,
transcription, image editing, offline
copilots) to become table stakes, with
enterprise policy deciding what runs
on device vs. in cloud.
Preparing for the Next
Wave of Compound
Semiconductor Scale-Up
ANIL VIJAYENDRAN, V P ,
Product Line Management,
Veeco
As we look toward 2026 and beyond,
several technology inflections are
reshaping the trajectory of compound
semiconductors—and positioning
Veeco for an important new phase of
growth. The most significant momentum is building around gallium
nitride (GaN), driven by demand for
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