ASIC Physical Design Methodology Engineer at NVIDIA

Shanghai, China

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Not SpecifiedCompensation

Mid-level (3 to 4 years)Experience Level

Full TimeJob Type

UnknownVisa

SemiconductorIndustries

Requirements

MS/PhD in Electrical or Computer Engineering with 2+ years industry experience
Understanding of standard cells/memory/IO/PLL and other hard IP modeling and their usage in the ASIC flow
Hands-on experience in advanced CMOS technologies, design with FinFET technology 7nm/5nm/3nm and beyond
Good knowledge with standard cell design & layout
Good knowledge of parameter extraction, device physics, STA methodology and EDA tools
Understanding spice analysis, crosstalk, electro-migration, noise, OCV, timing margins
Expertise in coding - TCL, Python, Perl
Familiarity with industry standard ASIC tools: LC, PT, Spice, etc
Strong communications skill and good teamwork experience

Responsibilities

Research and implement state-of-the-art timing signoff methodology on deep sub-micron process
Build automatic flow with commercial timing signoff tools to achieve high quality timing closure
Develop internal tools and methodology to automate timing constraint/SDC generation
Support the physical design implementation team for speed of light project execution
Develop and validate flows for ASIC backend library quality check, maintain and release methodology
Build and validate flows for design level lib cells usage auditing
Setup flows/methodology on library in deep submicron physical effects such as aging, self heating, etc

Skills

Key technologies and capabilities for this role

Timing AnalysisSTASDCTCLPythonPerlFinFET7nm5nm3nmSpiceLCPTOCVCrosstalkElectromigrationParameter Extraction

Questions & Answers

Common questions about this position

What education and experience are required for this role?

A MS/PhD in Electrical or Computer Engineering with 2+ years of industry experience is required.

What technical skills and knowledge are needed for this position?

Candidates need hands-on experience in advanced CMOS technologies like FinFET 7nm/5nm/3nm, good knowledge of standard cell design & layout, parameter extraction, device physics, STA methodology, EDA tools, spice analysis, crosstalk, electro-migration, noise, OCV, timing margins, and expertise in coding with TCL, Python, Perl, plus familiarity with tools like LC, PT, Spice.

What soft skills are important for this job?

Strong communication skills and good teamwork experience are required.

Is this a remote position or does it require office work?

This information is not specified in the job description.

What is the salary or compensation for this role?

This information is not specified in the job description.

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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