Experienced Module Integration Engineer

Samsung Electronics is hiring an Experienced Module Integration Engineer in Austin, United States. It pays $95k-$120k a year and Level rates it ; you can apply on Samsung Electronics.

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On Level's scale, this role is Little AI ●○○○: aI is not part of the work.
Level classifies it as a mid-level role.
Level lists 617 other open roles at Samsung Electronics.
The role was posted in the last few days.

AI in this role

About Samsung Austin Semiconductor
Samsung is a world leader in advanced semiconductor technology, founded on the belief that the pursuit of excellence creates a better world. At Samsung Austin Semiconductor, we are Innovating Today to Power the Devices of Tomorrow.

Come innovate with us!

Position Summary

We are seeking a motivated and high-caliber Module Integration Engineer to join our 2nm GAAFET team. This role focuses on the Front-End-of-Line (FEOL) integration, specifically targeting the RPMG (Replacement Metal Gate) and CTSDB (PC and NS isolation) modules.
As we transition from FinFET to GAAFET (Gate-All-Around FET) architecture, you will play a critical role in bridging the gap between unit process development and overall device performance. You will work at the intersection of lithography, etch, deposition, and device physics to ensure the successful integration of the gate stack and contact modules for the next generation of computing.

Role and Responsibilities

Here’s What You’ll Be Responsible For:

Module Ownership: Support the integration and optimization of the RPMG and CTSDB modules to meet target device specifications for the 2nm node. Duties will require daily and consistent trend monitoring and quick responsive actions when needed.
Process Integration: Collaborate with unit process engineers (Etch, CVD, ALD, CMP, Litho) to respond effectively to any module risks or issues related to process control and optimization.
Characterization & Analysis: Execute and analyze DOE (Design of Experiments) to evaluate the impact of process variations on electrical performance.
Failure Analysis: Understand advanced metrology and analysis data (TEM, SEM, AFM, EDX) for identify root causes of module failures and implement corrective actions.
Data Management: Leverage statistical software (e.g., Cloud Data Sources, Spotfire, Python, etc.) to analyze large datasets and correlate process parameters with device yield.
Cross-Functional Collaboration: Participate in design reviews and synchronization meetings with the Device and Layout teams to ensure manufacturability.

Skills and Qualifications

Here's what you'll need: Required Masters in Materials Science, Chemistry, Physical Chemistry, Physics or a closely related field (industry experience can substitute for advanced degree). Preference for Ph.D. in Materials Science, Chemistry, Physical Chemistry, Physics or a closely related field.·
2-3 years of relevant experience preferred, but not required for consideration.
Knowledge of MOSFET Physics (GAAFET preferred but FinFET also desired): Deep understanding of device physics, especially for interfacial physics (gate oxide, threshold voltage control, gate capacitance, performance and reliability etc.)
Process knowledge: Experience with or ability to understand various semiconductor process such as depositions (ALD/CVD), etch, and CMP, etc.
Programming: Intermediate proficiency in Python, R, SQL for automating data extraction and visualization.
Standard Office Software: High level of comfort and experience in Microsoft Excel and PPT. Excel skills should include basic understand and use of key data manipulation formulas such as VLOOKUP and MATCH.
Data Analysis Software: Proficiency in Spotfire, Minitab, or similar statistical software for yield and process analysis
Project management: Awareness or experience with various workflow styles like Waterfall and Agile, as well as experience with Software such as Confluence and Jira.

The current base salary range for this role is between $95,000 - $120,000. Individual base pay rates will depend on factors including duties, work location, education, skills, qualifications and experience. Total compensation for this position will include a competitive benefits package and may include participation in company incentive compensation programs, which are based on factors to include organizational and individual performance.

Total Rewards
At Samsung Austin Semiconductor, base pay is just one part of our total compensation package. The base compensation for this role will depend on education, experience, skills, and location.

We offer a comprehensive benefits package, including:

Medical, dental, and vision insurance
Life insurance and 401(k) matching with immediate vesting
Onsite café(s) and workout facilities
Paid maternity and paternity leave
Paid time off (PTO) + 2 personal holidays and 10 regular holidays
Wellness incentives and MORE

Eligible full-time employees (salaried or hourly) may also receive MBO bonuses based on company, division, and individual performance.

All positions at Samsung Austin Semiconductor are full-time on-site.

Trade Secrets
By submitting an application, you agree not to disclose to Samsung or induce or encourage Samsung to use any confidential or proprietary information (including trade secrets) belonging to a current or former employer or other entity.

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* Samsung Electronics America, Inc. and its subsidiaries are committed to Equal Employment Opportunity for all individuals regardless of race, color, religion, gender, age, national origin, marital status, sexual orientation, gender identity, status as a protected veteran, genetic information, status as a qualified individual with a disability, or any other characteristic protected by law.

How we rate this

Experienced Module Integration Engineer at Samsung Electronics rates 33 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.