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The Next-Gen AI-Powered EDA Integration Platform

The Next-Gen AI-Powered EDA Integration Platform

We don't replace your EDA stack — we unify it.

AI-powered workflow automation for CoWoS, FOPLP, and 2.5D/3D IC design.

We don't replace your EDA stack — we unify it.

AI-powered orchestration for CoWoS, 3DIC, and advanced packaging design.

InPack.AI EDA

Get in touch

Get in touch

Get in touch

Get in touch

View features

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the Next Generation of Semiconductor Design

the Next Generation of Semiconductor Design

”The semiconductor industry uses 10+ specialized EDA tools, each requiring months to master. InPack.AI is our answer — a unified AI platform that orchestrates your entire design workflow, from substrate to system, in one interface.”

”The semiconductor industry uses 10+ specialized EDA tools, each requiring months to master. InPack.AI is our answer — a unified AI platform that orchestrates your entire design workflow, from substrate to system, in one interface.”

user pic

Prof. Chi-Hua Yu (游濟華) | Co-founder & Head of LAiMM Lab

Prof. Chi-Hua Yu (游濟華) | Co-founder & Head of LAiMM Lab

Prof. Chi-Hua Yu (游濟華) | Co-founder & Head of LAiMM Lab

Our Features

Our Features

InPack . AI Platform:
Modular AI-Powered EDA

InPack.AI Platform: Modular AI-Powered EDA

We design, develop, and implement InPack.AI:
An EDA platform that helps IC design teams work smarter, not harder.

We design, develop, and implement InPack.AI:
An EDA platform that helps IC design teams work smarter, not harder.

Warpage Analysis

  • Simulation Configuration

    模擬配置

  • Geometry & Material Property

    幾何參數與材料特性

  • Data Generation & Processing

    數據生成與處理

  • AI Model & Performance

    AI 模型與性能驗證

  • DQN Reinforcement Learning

    DQN 強化學習應用

Warpage Analysis

  • Simulation Configuration

    模擬配置

  • Geometry & Material Property

    幾何參數與材料特性

  • Data Generation & Processing

    數據生成與處理

  • AI Model & Performance

    AI 模型與性能驗證

  • DQN Reinforcement Learning

    DQN 強化學習應用

AI-Powered Warpage Simulation

AI-Powered Warpage Simulation

AI-Powered Multi-Physics Analysis

AI-Powered Multi-Physics Analysis

AI-Powered Multi-Physics Analysis

Go beyond single-point warpage prediction. InPack.AI integrates thermal, mechanical, and electrical simulations in one unified platform — covering the entire design flow from substrate to CoWoS packaging. Achieve 600x acceleration with our ML-driven engine and 69.89% yield improvement through DQN-based parameter optimization.

Go beyond single-point warpage prediction. InPack.AI integrates thermal, mechanical, and electrical simulations in one unified platform — covering the entire design flow from substrate to CoWoS packaging. Achieve 600x acceleration with our ML-driven engine and 69.89% yield improvement through DQN-based parameter optimization.

Go beyond single-point warpage prediction. InPack.AI integrates thermal, mechanical, and electrical simulations in one unified platform — covering the entire design flow from substrate to CoWoS packaging. Achieve 600x acceleration with our ML-driven engine and 69.89% yield improvement through DQN-based parameter optimization.

Multi-Physics

Multi-Physics

CoWoS & 3DIC

CoWoS & 3DIC

ML + DQN

ML + DQN

InPack.AI

InPack.AI

AI Co-Design Studio

AI Co-Design Studio

AI Co-Design Studio

Design with AI, not just use it. Our AI Co-Design Studio lets you drag-and-drop EDA modules — from SoC synthesis to package routing — and orchestrate them into automated workflows. Describe your goal in natural language, and let our multi-agent system compose, execute, and optimize the entire design flow.

NeuroShine AI doesn't just predict; it creates.
Through a Deep Reinforcement Learning (DQN) framework, it accurately simulates multi-physics effects without a massive database, providing reliable scientific validation for your designs with an R² score of up to 0.983.

NeuroShine AI doesn't just predict; it creates.Through a Deep Reinforcement Learning (DQN) framework, it accurately simulates multi-physics effects without a massive database, providing reliable scientific validation for your designs with an R² score of up to 0.983.

AI Workflow

AI Workflow

Multi-Agent

Multi-Agent

Auto-Optimize

cGAN

cGAN

What can InPack.AI help with?

InPack.AI seamlessly integrates thermal, mechanical, and electrical simulations

|

Analyze

Simulation

Research

What can InPack.AI help with?

InPack.AI seamlessly integrates thermal, mechanical, and electrical simulations

|

Analyze

Simulation

NeuroShine Member Loading…

LAiMM Lab

NCKU

NeuroShine

NeuroShine Member Loading…

LAiMM Lab

NCKU

NeuroShine

Academic & Industry Collaboration

Academic & Industry Collaboration

Trusted by Academia & Industry Leaders

Trusted by Academia & Industry Leaders

Trusted by Academia & Industry Leaders

Born from LAiMM Lab at National Cheng Kung University, InPack.AI has been validated in real-world production with industry leaders including ASE and partners in the CoWoS/advanced packaging ecosystem. Our technology bridges cutting-edge AI research with practical semiconductor manufacturing needs.

Born from LAiMM Lab at National Cheng Kung University, InPack.AI has been validated in real-world production with industry leaders including ASE and partners in the CoWoS/advanced packaging ecosystem. Our technology bridges cutting-edge AI research with practical semiconductor manufacturing needs.

Born from LAiMM Lab at National Cheng Kung University, InPack.AI has been validated in real-world production with industry leaders including ASE and partners in the CoWoS/advanced packaging ecosystem. Our technology bridges cutting-edge AI research with practical semiconductor manufacturing needs.

Academic-backed

Academic-backed

Industry Collaboration

Industry Collaboration

Proven Reliability

Proven Reliability

Zero Disruption

Zero Disruption

Seamless EDA Tool Integration

Seamless EDA Tool Integration

Seamless EDA Tool Integration

InPack.AI works with your existing EDA stack — not against it. Connect Synopsys, Cadence, Ansys, and Zuken tools through our unified API layer. Deploy as cloud SaaS for instant access, or on-premise for maximum data security. Either way, your existing workflow stays intact while gaining AI superpowers.

InPack.AI works with your existing EDA stack — not against it. Connect Synopsys, Cadence, Ansys, and Zuken tools through our unified API layer. Deploy as cloud SaaS for instant access, or on-premise for maximum data security. Either way, your existing workflow stays intact while gaining AI superpowers.

InPack.AI works with your existing EDA stack — not against it. Connect Synopsys, Cadence, Ansys, and Zuken tools through our unified API layer. Deploy as cloud SaaS for instant access, or on-premise for maximum data security. Either way, your existing workflow stays intact while gaining AI superpowers.

Synopsys / Cadence / Ansys

Synopsys / Cadence / Ansys

On-Premise Option

On-Premise Option

API Integration

API Integration

Cloud-Native Solution

Here is your IC-Design Workflow

Ongoing Project :

InPack.AI EDA Tool

90% Finished

MLOps Pipeline

Modeling

Simulation

Prediction

Optimization

Geometry Definition

Material Characterization

Cloud-Native Solution

Here is your IC-Design Workflow

Ongoing Project :

InPack.AI EDA Tool

90% Finished

MLOps Pipeline

Modeling

Simulation

Prediction

Optimization

Geometry Definition

Material Characterization

core competencies

core competencies

The Six Pillars of InPackAI Flow Suite

The Six Pillars of InPackAI Flow Suite

InPackAI Flow Suite: Empowered by Six Technological Pillars

InPackAI Flow Suite: Empowered by Six Technological Pillars

AI-Powered Inference

Millisecond-level warpage & stress prediction, replacing time-consuming FEM simulations.

AI-Driven Routing

Automated RDL and 3DIC path generation to accelerate complex packaging layouts.

Workflow Orchestration

Modular "Lego-style" architecture to seamlessly connect fragmented EDA stages.

EDA Tool Bridge

Seamless data conversion and integration across Synopsys, Cadence, and Ansys.

AI Co-pilot

Natural language task dispatching powered by advanced Skill + Memory framework.

Microchannel Thermal CFD

Advanced fluid dynamics simulation for extreme heat management in HPC designs.

Our Process

Our Process

Our Simple, Smart, and Scalable EDA Process

Our Simple, Smart, and Scalable EDA Process

We design, develop, and implement InPack.AI to help IC design teams work smarter, not harder.

We design, develop, and implement InPack.AI to help IC design teams work smarter, not harder.

Step 1

Connect Your Tools

Connect Your Tools

Integrate with your existing Synopsys, Cadence, and Ansys environments. InPack.AI acts as a unified orchestration layer.

Integrate with your existing Synopsys, Cadence, and Ansys environments. InPack.AI acts as a unified orchestration layer.

Analyzing current workflow...

Geometry check

Material check

Boundary check

Speed check

Model Build

Step 2

Define Your Workflow

Define Your Workflow

Drag-and-drop modules in our AI Co-Design canvas.

Or simply tell our AI assistant what you need:

"Build a CoWoS thermal-mechanical analysis flow"

Drag-and-drop modules in our AI Co-Design canvas.

Or simply tell our AI assistant what you need:

"Build a CoWoS thermal-mechanical analysis flow"

Drag-and-drop modules in our AI Co-Design canvas.Or simply tell our AI assistant what you need:"Build a CoWoS thermal-mechanical analysis flow"

  • class AutomationTrigger:
    def __init__(self, threshold):
    self.threshold = threshold
    self.status = "inactive"

    def check_trigger(self, value):
    if value > self.threshold:
    self.status = "active"
    return "Automation triggered!"
    else:
    return "No action taken."
    def get_status(self):
    return f"Status: {self.status}"

  • class AutomationTrigger:
    def __init__(self, threshold):
    self.threshold = threshold
    self.status = "inactive"

    def check_trigger(self, value):
    if value > self.threshold:
    self.status = "active"
    return "Automation triggered!"
    else:
    return "No action taken."
    def get_status(self):
    return f"Status: {self.status}"

  • class AutomationTrigger:
    def __init__(self, threshold):
    self.threshold = threshold
    self.status = "inactive"

    def check_trigger(self, value):
    if value > self.threshold:
    self.status = "active"
    return "Automation triggered!"
    else:
    return "No action taken."
    def get_status(self):
    return f"Status: {self.status}"

  • class AutomationTrigger:
    def __init__(self, threshold):
    self.threshold = threshold
    self.status = "inactive"

    def check_trigger(self, value):
    if value > self.threshold:
    self.status = "active"
    return "Automation triggered!"
    else:
    return "No action taken."
    def get_status(self):
    return f"Status: {self.status}"

Step 3

AI Executes & Optimizes

AI Executes & Optimizes

Our Multi-Agent system calls the right tools automatically.

• ML models predict results in seconds

• DQN optimizes parameters continuously

• 600x faster than traditional simulation

Our Multi-Agent system calls the right tools automatically.

• ML models predict results in seconds

• DQN optimizes parameters continuously

• 600x faster than traditional simulation

Our solution

Your stack

Step 4

Get AI-Generated Reports

Get AI-Generated Reports

Receive comprehensive analysis reports, auto-generated by AI. Export to PDF, share with your team, iterate quickly.

Receive comprehensive analysis reports, auto-generated by AI. Export to PDF, share with your team, iterate quickly.

Warpage improvement rate

Speed will increase by 20%

Workflow system

Update available..

Version control

Up to date

Benefits

Benefits

Why Engineering Teams Choose InPack.AI

Why Engineering Teams Choose InPack.AI

Discover how InPack.AI enhances efficiency, reduces costs, and drives design simulation with smarter, faster processes.

Discover how InPack.AI enhances efficiency, reduces costs, and drives design simulation with smarter, faster processes.

End Tool Fragmentation

Before: Switch between 10+ tools, 10+ file formats. Now, one unified platform, seamless data flow

End Tool Fragmentation

Before: Switch between 10+ tools, 10+ file formats. Now, one unified platform, seamless data flow

Multi-Physics Integration

Seamlessly integrates multi-physics analysis on one platform, eliminating tool fragmentation.

Multi-Physics Integration

Seamlessly integrates multi-physics analysis on one platform, eliminating tool fragmentation.

24/7 Availability

AI-powered systems operate around the clock, ensuring seamless support and execution without downtime.

24/7 Availability

AI-powered systems operate around the clock, ensuring seamless support and execution without downtime.

Cost Reduction

AI automates routing and parameter settings, reducing manual intervention, and optimizing resource allocation.

Cost Reduction

AI automates routing and parameter settings, reducing manual intervention, and optimizing resource allocation.

Data-Driven Insights

Leverage AI to analyze large datasets, identify trends, and make smarter, faster, and more accurate design decisions.

Data-Driven Insights

Leverage AI to analyze large datasets, identify trends, and make smarter, faster, and more accurate design decisions.

Predictive Accuracy

InPack.AI core engine performs instant, accurate predictions in warpage analysis, providing a reliable basis for your decisions.

Predictive Accuracy

InPack.AI core engine performs instant, accurate predictions in warpage analysis, providing a reliable basis for your decisions.

About us

Who We Are

Who We Are

NeuroShine was founded with a vision:

to build Taiwan's answer to the fragmented EDA landscape.

NeuroShine was founded with a vision:

to build Taiwan's answer to the fragmented EDA landscape.

user pic

Chi-Hua Yu

An Associate Professor at NCKU specializing in AI bionics and multi-physics simulation technologies.

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Chi-Hua Yu

An Associate Professor at NCKU specializing in AI bionics and multi-physics simulation technologies.

user pic

Di-Chun Hu

A founding pioneer of Taiwan's LCD display industry with a Ph.D. from MIT's Department of Materials Science and Engineering.

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Di-Chun Hu

A founding pioneer of Taiwan's LCD display industry with a Ph.D. from MIT's Department of Materials Science and Engineering.

Shin-Ruei Lin

A senior software development expert from TSMC, specializing in numerical simulation methods and industrial software design.

Shin-Ruei Lin

A senior software development expert from TSMC, specializing in numerical simulation methods and industrial software design.

Jayson Ng

An ex-backend engineer from the finance industry, with a Master's degree in Computer Science from Columbia University.

Jayson Ng

An ex-backend engineer from the finance industry, with a Master's degree in Computer Science from Columbia University.

Contact

Get in Touch with Us

Get in Touch with Us

Get in Touch with Us

Have questions or need EDA AI solutions?
Let us know , and we’ll be in touch!

Have questions or need EDA AI solutions?
Let us know , and we’ll be in touch!

E-mail

E-mail

syuanku@neuroshine.co

syuanku@neuroshine.co

FAQ's

Frequently Asked Questions

Find quick answers to the most common support questions

How does InPack.AI EDA handle multi-physics simulation?

Is our design data secure?

Can InPack.AI integrate with our existing EDA workflow?

Besides warpage, what other specific problems can your tool solve?

What kind of support can we expect when using InPack.AI?

FAQ's

Frequently Asked Questions

Find quick answers to the most common support questions

How does InPack.AI EDA handle multi-physics simulation?

Is our design data secure?

Can InPack.AI integrate with our existing EDA workflow?

Besides warpage, what other specific problems can your tool solve?

What kind of support can we expect when using InPack.AI?

FAQ's

Frequently Asked Questions

Find quick answers to the most common support questions

How does InPack.AI EDA handle multi-physics simulation?

Is our design data secure?

Can InPack.AI integrate with our existing EDA workflow?

Besides warpage, what other specific problems can your tool solve?

What kind of support can we expect when using InPack.AI?

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