Andrew Luu

R&D Process Engineer

Energy | Biotechnology

andrewluu.engr@gmail.com

1-408-712-5798

Resume

INTRODUCTION

Hey! I’m Andrew. I have about 4 years of industrial process development experience and a total 8 years of combined hands-on work and research experience.

My experience spans across energy, biotechnology, and computational modeling, from oligonucleotide processing to high-volume solar metallization.

I am experienced in applying practical, data-driven, and lean methods to develop processes that help people and the planet.

PROFESSIONAL OVERVIEW

CAREER MILESTONES

2026

ANTORA ENERGY

Process Integration Engineer, Thermophotovoltaics

2026

AMERICAN SOCIETY FOR QUALITY

Six Sigma Black Belt Certification

2024

MAXEON SOLAR TECHNOLOGIES

Process Development Engineer, Metallization

2023

NORTHEASTERN UNIVERSITY

M.S. Chemical Engineering

2020

AGILENT TECHNOLOGIES

Process Development Associate, RNA Oligos

2020

UNIVERSITY OF CALIFORNIA, IRVINE

B.S. Chemical Engineering

B.S. Chemistry

2019

US DOE - NATIONAL ENERGY TECHNOLOGY LABORATORY

Research Intern, Redox Geochemistry

KEY COMPETENCIES

8 years of hands-on engineering experience, from undergraduate research laboratories to industrial process development in energy, materials science and life sciences.

Six Sigma Black Belt with applied experience in DOE, SPC, statistical analysis, RCA, PFMEA, and 5S.

Familiar with collaboration in fast-paced, multidisciplinary R&D environments through ideation, prototyping, validation, and optimization.

SKILLS & EXPERIENCE

PROCESS DEVELOPMENT

Sectors — Semiconductors (solar), biotechnology (oligonucleotides), environmental (gas).

Methods — Design for Six Sigma (DFSS), lean manufacturing, PFMEA, root cause analysis, IQ/OQ/PQ, SOP documentation.

DATA ANALYSIS

Statistical analysis — Design of experiments (DOE), Student’s t-test, ANOVA, regression, multivariate model fitting, sample sizing, alpha-risk stochastic modeling.

Process control — SPC, process capability studies, measurement systems analyses (MSA).

COMPUTATIONAL SKILLS

Software — SolidWorks 3D CAD, JMP, Python, MATLAB, Wolfram Mathematica, Ansys Fluent, Aspen HYSYS, ChemDraw, CODESYS, MS Office.

Methods — AI literacy and prompting, finite-element analysis, Monte-Carlo simulation, molecular dynamics.

CHARACTERIZATION METHODS

Optical — Automated optical inspection systems, optical microscopy.

Mechanical — Profilometry, tensile testing.

Electrical — Lock-in thermography (LIT), IV curve testing, cyclic voltammetry (CV).

Spectroscopic — FT-IR, UV-Vis, electroluminescence (EL), NMR.

Other — SEM/TEM, XRD, LCMS/GCMS, thermogravimetric analysis (TGA).

OPERATIONAL EXPERIENCE

Environments — Semiconductor cleanroom, CGMP life sciences manufacturing, air-free systems (Ar/N2 glovebox), high-temperature systems (+400°C), vacuum systems (PVD, PECVD), chemical wet bench.

Semiconductors — Electromechanical systems, wafer pick-and-place, wet-etch lithography, inkjet printing, femtosecond laser ablation, metal extrusion, thin-film deposition.

Biotechnology — Organic synthesis, HPLC, RPLC, IEX, automated liquid handlers, liquid-liquid extraction, diafiltration, lyophilization.

DEGREES, CERTIFICATIONS, & HONORS

DEGREES

M.S., Chemical Engineering

Northeastern University · 2023

B.S., Chemical Engineering

University of California, Irvine · 2020

B.S., Chemistry

University of California, Irvine · 2020

CERTIFICATIONS

Six Sigma Black Belt

Credential ID 177766354

American Society for Quality · 2026

Engineer-In-Training, Chemical

Certificate No. 171391

National Council of Examiners for

Engineering and Surveying · 2020

HONORS

Best Chemical Engineering

Teaching Assistant

Northeastern University · 2023

Phi Lambda Upsilon Honors in Chemistry

University of California, Irvine · 2020

Mickey Leland Energy Fellowship

U.S. Department of Energy · 2019

Boeing Scholarship

The Boeing Company · 2019

SELECTED WORK EXAMPLES

01 / MAXEON solar technologies

Solar module manufacturing pilot line deployment

I was part of a team that was tasked with scaling a next-generation photovoltaic (PV) metallization technology from a bench-scale laboratory environment to a high-throughput pilot manufacturing line. Our initial startup deployment unsurprisingly faced various challenges as we strove to deploy this novel technology at scale.

As a process engineer, I primarily contributed to end-to-end process optimization and line integration. This involved work such as statistical process control (SPC) analyses for performance benchmarking and Pareto analyses to systematically target and eliminate root causes of defects. I also helped to establish custom terminology, localized measurement techniques, and standardized specification limits for inter-process hand-offs.

Through our work, we stabilized the pilot line, enabling the execution of Site Acceptance Testing (SAT) and operational hand-off to the production manufacturing engineering team.

Wet etch lithography process optimization

This project was focused on reviving a legacy wet etch lithographic process for renewed exploratory R&D. The process had been discontinued for several years, resulting in the loss of institutional knowledge and expert guidance from when the line was originally established, resulting in metallized solar cells facing high defect rates driven by severe process variation.

I assumed process ownership of the lithography line to re-baseline the process. I conducted etch rate studies to map the multi-variable relationships between etchant concentration, temperature, and etch rates. I then scoped out practical operational process windows, executed a targeted Design of Experiments (DOE) to pinpoint optimal operating parameters, and authored a standardized Standard Operating Procedure (SOP) for operator use.

This work stabilized our revived legacy line and systematically minimized cell defect rates.

Pick-and-place statistical feedback control

Part of the deployment of our pilot line involved an automated water transfer step, which required precise placement accuracy, but was highly sensitive to positional drift from environment variables such as thermal expansion.

I was heavily involved in the development and deployment of a real-time feedback loop using downstream optical metrology to correct process mean shifts without overreacting to process noise. We utilized an incremental one-sample t-test algorithm to continuously evaluate positional error data and calculate the statistical likelihood of a true process mean shift, which would then trigger a feedback correction when the p-value fell below a specified threshold (usually an alpha risk of 0.05). I collaborated closely with PLC programmers to connect the feedback data with robotic actuator logic and validated the control loop performance following its deployment.

This work was necessary for proper deployment of our pilot line and enabled continued work on other downstream processes.