Applying Artificial Intelligence in FabTechnology Co-Optimization

Silvaco At A Glance

Enabling semiconductor innovation from atoms to systems

Silvaco Leadership

Design, Simulate, Analyze, and Verify
from Concept to Complete Product Yield

600+

Customers

200+

University Customers

260+

Employees

TCAD

TCAD

EDA

SIP

EDA

Global Presence

Headquarters &Development Center
Silicon Valley, USA

SIP

Automotive

IoT

5G / 6G

Display

MemoryCopyright │ ©2024 Silvaco, Inc.

HPC

Power2

From Atoms To Systems Platform
Foundation
IP

Systems

InterfaceIPIP
ManagementCircuits
Design Technology Co-Optimization (DTCO)
Silvaco SIP

DevicesFab Technology Co-Optimization (FTCO)

Processes

Atoms

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Silvaco EDA

Silvaco TCAD

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Victory TCAD Platform

Develop semiconductor processes and devices prior to and during manufacturing
VICO T RY T CAD PLAT FO RM→

Model and simulate the manufacturing process→

Model and simulate semiconductor and photonic
device behavior→

Explore trade-offs in performance, power,size and reliability→

Accelerate time to yield→

Process

DeviceModel

Process Simulation
Solid Modeling

Device Simulation

Modeling

Victory Process
Process Simulation

Victory Device

Utmost IV

SPICE Model generation

Device SimulationVictory RCx

Parasitics Extraction

Co-Optimize between fab and design
Victory MeshMeshing, Solid Meshing

Victory Atomistic
Nanotech Modeling

SmartSpiceCircuit Simulation

Design T echnology Co-O pt imiz at ion ( DT CO )Co- optim iza t ion between Fab and Des ign
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Motivation for Application of AI in Fab Process Optimization

Problem

Consequences

Solution• Fabs are getting more complex• Traditional methods of optimizing
fab processes is slow

• Application of Digital Twin

• Running DoE with wafers tooptimize fabs not scaling• No two fab lines are identical

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• Impact on time-to-yield andprofitability• Costly and time-consuming tobring up a new fab line

technology and AI to accelerate
fab process optimization• Saves costs and accelerates fabbring up• Works with Memory, CMOS, SiC,GaN, etc.

5Four Levels of AI in EDA Industry
Self-Optimizing Tools

Design AssistantGenerative Design

Fab Assistant

Improve Performance ofExisting Design Tools• Training for specificcapability• Reinforcement learning• Efficiency improvement• Optimum results in lesstime

Build Foundation Models
From Design Experience• Machine learning
foundation models• Learns about design andstyle• Design strategy
recommendations• Understanding of intent

Expand AssistantCapabilities• Deep learning on mega data
• Generate new data
• Self-supervised learning• Design generated fromrequirements

Build Digital Twin of FabProcess• Use manufacturing data todevelop Digital Twin of Fab• Builds a Real-Time model ofFab Process• Fab engineers run
experiments on desktop• Identify issues that impact
yield, faster and earlier

Source: DAC 2023 Keynote, Wally Rhines, President & CEO Cornami, Inc& Silvaco independent director
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Silvaco6Digital Twin

A physics-based digital counterpart that mirrors the form, fit and function of the physical world“Digital Twin” concept was introduced over 20 years ago asa product Lifecycle Management (PLM) concept.
•A Digital Twin is a virtual representation of complex realsystem

Real WorldDigital Twin
Data– Components, devices, or full fab process– Designed to model and be indistinguishable from the real-world system– Provides insight into how the behavior of the real system willchange with variation in design••A Digital Twin can start to form prior to the real system,
and can allow for virtual prototyping

Information
Insight

Once formed, the digital twin allows engineers to ask
the digital twin “what-if” scenarios

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Exploring The Digital Twin Concept with TCAD

• Silvaco’s physics-based simulators virtualizes

semiconductor and photonics manufacturing
and device characterization• Allows organizations to maintain a “digital twin”
DRAM Process Flowof their semiconductor process and devices• Accurate digital twins can focus on predictive

power rather than on real time monitoring
FIN Process Flow with LER
BEOL Process FlowCopyright │ ©2024 Silvaco, Inc.

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Silvaco’s Machine Learning Accelerates Semiconductor Digital Twin
DevelopmentSampling &Digital Twin

Data Generation

Data Mining,Analysis,
Visualization

AI-Driven
Nonlinear
Modeling

AI-Based Optimized
Digital Twin Model• Real Fab/Electrical Data and Physics-Based Simulation generates training data
– ML training is done using simulated structure or real fab data
– Simulation-efficient state of art DoE algorithm– Advanced 3D metrology (min distance, overlap area, hotspot …etc.)• ML algorithms help refine digital twin model accuracy– Advanced neural network– Decision tree algorithm for outlier detection– Dominant input screen by advanced statistical method• Integrated solution– Both simulation and ML analysis are under the same GUI framework
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Silvaco’s AI-Driven Fab Technology Co-Optimization (FTCO )1

Removing cost of trial-and-error using AI-driven models through digital twins

Sampling & Data
Generation

Advanced CMOS

→Broad application in the semiconductor processes• CMOS, SiC, GaN, Memory

Data Mining, Analysis,Visualization

Physical SimulationAI-Optimized
Digital Twin

AI-Driven
Nonlinear ModelingM O DEL

AI customizes solutions for fabs automatically

DoE Setup

VirtualExperimentation
Electron Microscope
View

GOOD→Wafer
UNT IL G O O D M O DEL

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Interactive Structure
Modeling
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FTCO

Solution Platform

Tool Components
ML/AI Analysis Tools

Victory Analytics• Machine Learning analytics solution designed for the semiconductor industry. Tool iscomprised of data visualization, input feature selection and data training, modeling,
optimization and DOE.
DoE Environment
Victory DoE• Project Manager and Design of Experiment toolCore Data for building a Digital Twin
Victory Process• Process Simulator - Build virtual fab flows of devices and components• Stress Simulator - Understand how stress impacts the fab flow step-by-step
Victory Device• Device Simulator – Understand Electrical Characteristics of the Devices
Victory RCx• Parasitic Field Solver that explores fine details of the BEOL processCopyright │ ©2024 Silvaco, Inc.

Interactive
Digital Twin
Model

Flow of Work

Advanced DoE& Machine Learning

Experimental
Fab/Device Data

Physics-BasedSimulations

11FTCODevelopment Partnership with Micron“Micron is excited to strengthen ourpartnership with Silvaco. Silvaco’s AI anddigital twin solution is enabling us toaccelerate our groundbreaking
advancements in memory and storage.”
Dr. Gurtej Sandhu
Principal fellow of Technology Pathfinding
Micron Technology, Inc.

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FTCOSummary• The growing fab complexity requires a new approach to process optimization• We have combined our deep TCAD expertise and semiconductor knowledge

with AI to develop a unique platform for fab process optimization• We partnered with Micron Technology for the development of FTCO Platform• FTCO Platform accelerates process and yield optimization• FTCO saves cost by enabling fast experimentation and reducing the need torun physical wafers

FTCO

Solution Platform• FTCO has broad application in the semiconductor processes - CMOS, SiC,GaN, Memory
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Thank you.

Copyright │ ©2024 Silvaco, Inc.