Outlook Supplement - Flipbook - Page 9
Forecast
Only 4 years ago, in our contribution
to the Semiconductor Digest 2022
forecast, we cited an observation from
the Decadal Plan for Semiconductors
by the Semiconductor Research
Corporation that at current growth
rates power consumed by computing
was on track to exceed global energy
production sometime between 2040 and
2050. And that was before the current
boom in AI. We are already seeing this
issue enter the public consciousness as
communities struggle to share power
resources equitably between computing
centers and other individual, corporate,
and institutional consumers. Even in
this arena sophisticated technologies
in the sub fab will play a critical role
in supporting advanced processes and
architectures that improve the energy
efficiency of computing, such as our
current support for the advanced
lithographic technologies used to create
nanoscale devices.
Industry Transformation
and Innovation in 2026
JOHN MACULLEY, G l o b a l H i g h
Te c h In d u s t r y St r at e gi s t ,
Dassault Systèmes
The semiconductor
industry is undergoing
an exponential transformation, driven by rising
complexity, emerging
technologies, and
shifting global demand.
Advanced process
JOHN MACULLEY
nodes are projected to
achieve 2 nm by 2025,
with research targeting angstrom-level
precision. Looking ahead to 2026, innovations like 3D packaging, quantum
computing, and AI accelerators are
shaping the next generation of chips,
while executives focus on reducing
costs and accelerating time-to-market.
• Increased AI-driven design &
manufacturability: Coordinating
equipment development with chip
designs, process technologies, and
www.semiconductordigest.com
materials is increasingly critical for
faster time-to-yield and improved
system-level reliability. In 2026,
we expect that AI-driven virtual
twin simulations and Model-Based
Systems Engineering (MBSE)
approaches will enable companies to optimize designs digitally,
design for manufacturability, reduce
reliance on physical prototypes, and
improve systems performance.
• Geopolitics, supply chain & ecosystem collaboration: Restricted
access to advanced chip-making
technologies and global supply chain
pressures are creating four distinct
and sovereign semiconductor ecosystems in the U.S., Europe, Asia,
and China. In 2026, collaboration,
technology co-optimization, and integrated platforms across these ecosystems will be essential to ensure
resilience, security, and competitive
advantage.
• Surging demand for AI and
High-[Performance Computing,
IP management: Rising demand
for AI accelerators, such as GPUs,
and high-performance computing
is driving a focus on energy efficiency, thermal management, and
system-level performance. Looking
forward, fully integrated, cloudbased engineering platforms will
help manage data, protect intellectual property, maintain traceability,
and reduce costly misalignment or
late-stage revisions across organizations and value chains.
Edge AI Reaches Its
Scalable Future
MARK REITEN, C o r p o r a t e V i c e
President, Licensing, MPD
and E AI Business Units,
Mic r o c hip Te c h n o l o g y
The edge AI landscape is evolving
faster than ever, and 2026 will be the
year organizations shift from proofof-concept thinking to full production
deployment. Across every major market
segment, engineering
teams are moving
beyond experiments
and adopting edge
intelligence as a foundational part of their
system architecture.
This shift is driven by
MARK REITEN
clear improvements in
performance, power efficiency, security
and the availability of tools that simplify
development on resource constrained
devices.
Companies have spent the past
several years evaluating how AI can
improve reliability, reduce operating
costs and strengthen user experiences. In 2026, the focus will swing
toward operationalizing these ideas.
Designers are selecting embedded
processors, MCUs, FPGAs and MPUs
capable of running inference close
to the sensor with consistent performance and predictable power draw.
Success at the edge will come from
solutions that pair the right compute
with streamlined developer tools and
the long product life cycles needed to
support multi-year platforms. Strong
technical and FAE support will also
play a critical role as teams look for
suppliers who can guide them through
both AI concepts and practical system
constraints.
The industry is moving away from
large, generic models and toward
tightly optimized networks tailored
for low-power devices. Quantization,
pruning and architecture search are
becoming standard practice as engineers seek models that fit within
kilobytes or a few megabytes while still
delivering the accuracy their applications demand. Toolchains that simplify
optimization, compression, evaluation
and deployment will accelerate this
trend throughout 2026, especially when
paired with compute platforms that are
easy to use and supported by reliable,
long-term software ecosystems.
Keeping data local provides inherent
Semiconductor Digest Supplement to January 2026
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