Ph.D. · Technology Architect, IBM Research
Defining the manufacturing blueprint for next-generation 2nm nanosheet technology. My work spans semiconductor devices, advanced packaging, AI hardware acceleration, and manufacturable qubit devices — bridging material science with system-level impact.
Defining next-generation nanosheet (GAA) transistor architectures at the 2nm node, co-optimizing process integration, lithography, and etch for optimal PPA in logic standard cells.
Developing CMOS-integrated TaOx and HfOx resistive RAM on 65nm platforms for energy-efficient neuromorphic and in-memory computing, benchmarked against neural network accuracy targets.
Heterogeneous integration and advanced packaging solutions — signal/power integrity, thermal management, and bump pitch scaling roadmaps for packaged semiconductor systems.
Hardware-aware neural network training and inference on RRAM arrays, spanning spiking neural networks, error-correcting codes, and space-grade low-power AI applications.
Research into manufacturable qubit devices compatible with industry-scale 300mm wafer platforms, bridging quantum hardware fabrication with semiconductor process integration.
Polymer microfluidic integration with silicon photonic biosensors for cost-effective clinical diagnostics, including COVID-19 antibody detection platforms.
Technology architect defining the 2nm nanosheet manufacturing blueprint. Leading Power-Performance-Area optimization across logic nodes including next-generation Gate-All-Around transistors (IBM–Rapidus collaboration). Responsibilities span BEOL scaling, standard cell design (inverter, NAND, NOR), advanced packaging PPA, and product-level performance and yield evaluation.
Developed and characterized CMOS-integrated TaOx/HfOx RRAM devices for neuromorphic AI accelerators. Electrical characterization from 300ps to 10µs pulse widths; benchmarked device performance using the IBM AIHWKit simulator against neural network accuracy targets.
Led hardware inference demonstration for COVID-19 antibody detection using an 8×8 RRAM array. Trained ANN and SNN models with 6-bit quantization, validated against PCR ground truth datasets.
Designed and characterized polymer microfluidic chips integrated with silicon photonic ring resonator biosensors for Lab-on-a-Chip clinical diagnostics, resulting in a featured HOT article in Lab on a Chip (2021).
Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning
Nature Scientific Reports, 13, 14963 · M. Abedin, N. Gong, K. Beckmann et al.
MR-PIPA: An Integrated Multi-level RRAM (HfOx) based Processing-In-Pixel Accelerator
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits · M. Abedin et al.
Disposable photonics for cost-effective clinical bioassays: application to COVID-19 antibody testing
Lab on a Chip, 21(15):2913–2921 · HOT Article 2021 · J.S. Cognetti, D.J. Steiner, M. Abedin et al.
Enhanced Computational Study with Experimental Correlation on I–V Characteristics of Tantalum Oxide (TaOx) Memristor Devices in a 1T1R Configuration
Wiley Small · S. Sihn, W. Chambers, M. Abedin et al.
DTCO Guided Process Integration: Case Studies From FEOL & BEOL with BSPDN
2024 SEMI ASMC · M. Abedin, S. Khan, N. Lanzillo, D. Dechene
In-memory Computation of Error-Correcting Codes Using a Reconfigurable HfOx RRAM 1T1R Array
IEEE MWSCAS 2021, pp. 593–598 · M. Abedin et al.
Photonic Biosensors for Diagnostics
WO 2022/164982 A1 · CA 3209078 A1 · N. Cady, N. Tokranova, M. Abedin et al.
Source/Drain Extension with Spacer Layers; Wrap-Around Contact Having Non-Uniform Thickness; Backside Gate Contact & Cross-Couple Connect
USPTO · M. Abedin, R. Xie, T. Li, J. Frougier et al. · 15+ additional patents pending
Awarded to ~0.36% of IBM engineers companywide for inventions of significant importance, IP portfolio contribution, and mentoring innovators.
Recognizing outstanding performance and defining contribution to a significant project outcome — received within two years of joining IBM.
Won $10,000 IEEE award (one of 3–4 awarded globally) to organize the Semiconductor & Quantum Summer School for students and professionals in Schenectady, NY.
Selected by IBM senior managers for this nationally recognized leadership development program in recognition of planning, execution, and training of young engineers.
Awarded $15,000 for a neuromorphic computing proposal for low-power, low-latency space applications, sponsored by the U.S. Space Force and Air Force Research Lab.
Article on disposable photonics for COVID-19 antibody testing recognized as a highly influential publication of 2021 by the journal editors and referees.
Awarded $1,000 each year by the College of Nanotechnology, Science, and Engineering (University at Albany) to support graduate scholarly opportunities in nanotechnology.
Talk on "Predicting COVID-19 Infection Using RRAM-enabled Neuromorphic Hardware and Multiplexed Antibody Testing" recognized at the IEEE / IBM AI Hardware Center symposium.
Nanoscale Engineering
Resistive RAM Arrays for energy-efficient computing
Nanoscale Engineering
Microfluidics for optical sensors (Lab-on-a-Chip)
Electrical & Electronics Engineering
Novel pixel circuit design for wide dynamic range CMOS image sensors
Open to research collaborations, mentorship inquiries, speaking invitations, and academic opportunities. Always happy to connect with students and researchers working in semiconductor technology, AI hardware, or quantum devices.