About

Benyamin Davaji is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University, where he joined the faculty in January 2022. His research focuses on MEMS and integrated microsystems with an emphasis on sensing and computation using mechanical waves; acoustic and ultrasound transducers; BioMEMS and bio-interfaces; microcalorimetry; and Digital Twins for semiconductor processes and printed electronics, nanofabrication, and semiconductor manufacturing. He is involved in interdisciplinary research through the Autonomous Integrated Microsystems (AIMS) Laboratory, which explores the development of novel integrated microsystems based on MEMS, ultrasound, and calorimetric technologies and devices. His work combines physics and data science, including AI and machine learning, to discover and invent sensors, micro-actuators, and computational devices via nanofabrication and advanced manufacturing. Davaji holds a Ph.D. in Electrical and Computer Engineering from Marquette University, obtained in 2016, and has completed a postdoctoral associate position at Cornell University. His contributions have been recognized with awards such as the 2026 FLEXI Award for Technology Leadership and the Outstanding Faculty Service Award, reflecting his leadership in advancing digital twins, AI for flexible electronics manufacturing, and innovative sensing technologies.

Research topics

• Physics
• Materials science
• Chemistry
• Computer Science
• Nanotechnology
• Optics
• Computer vision
• Optoelectronics
• Acoustics
• Condensed matter physics
• Microbiology
• Biology
• Algorithm
• Computer graphics (images)
• Chromatography
• Electrical engineering

Selected publications

• Thermal Drift Compensation in Uncooled NEM IR Pixels via Multimode Operation

  2026-01-25

  articleSenior author

  This paper presents a thermal drift compensation approach for an uncooled piezoelectric NEMS infrared (IR) pixel based on multiple acoustic mode operation. This method exploits two distinct resonance frequencies (two nanomechanical modes) of an AlScN-based IR resonator, each with different IR responsivities, assessed under varying temperatures and IR power conditions. Monitoring the frequency shifts of these two modes (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f_{1}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f_{2}$</tex>) in the admittance curve of a nanomechanical resonator under several temperature conditions enables us to detect the IR power level compensated for thermal drift without the need for any dark time for calibration. A linear model with a multiplicative interaction term is employed to explain the relation between temperature changes, IR responsivities, and frequency shifts at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f_{1}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f_{2}$</tex>. Using this model, the normalized mean absolute errors of the computed incident IR power by the nano pixel were 13.46 % at 25° C and as 8.68 % at 35° C.

  PublisherDOI

• Bulge Test for Direct Mechanical Characterization of ALN and Scaln Thin Films

  2026-01-25

  articleSenior author

  This study presents the mechanical characterization of AlN and 30%-ScAlN thin-film membranes using a bulgetest setup developed for rectangular geometries. A controlled air-pressure system and digital holographic microscopy were used to measure pressure-deflection behavior and extract the in-plane biaxial modulus. Both materials showed reversible deformation with minimal hysteresis, as loading and unloading curves almost overlapped. The extracted biaxial modulus was about 320 GPa for AlN and 200 GPa for 30%-ScAlN, consistent with the softening effect of Sc incorporation. Size-dependent deflection trends were also observed. The method enables a reliable comparison of material and geometric effects in nitride-based MEMS membranes.

  PublisherDOI

• Monolithic Integration of a Dual-Mode On-Chip Antenna with a Ferroelectric Hafnium Zirconium Oxide Varactor for Reprogrammable Radio-Frequency Front Ends

  Preprints.org · 2026-01-08

  preprintOpen access

  In this work, we report a dual-mode ferroelectrically programmable on-chip antenna. The antenna is built on a silicon wafer using Complementary Metal-Oxide-Semiconductor (CMOS) processes and exhibits two programmable resonant modes: one in the super high frequency (SHF) range and one in the extremely high frequency (EHF) range. The SHF mode resonates at 8.5 GHz and exhibits ultrawideband (UWB) behavior, while the EHF mode resonates at 36.6 GHz. Both resonance frequencies can be tuned in a non-volatile fashion by controlling the ferroelectric polarization state of a Hafnium Zirconium Oxide (HZO) varactor monolithically integrated into the feed line. This programmability arises from the ferroelectric switching of the embedded HZO film, which results in a non-volatile variation of its permittivity upon application of a voltage pulse. Ferroelectric switching occurs at approximately ±3 V and induces maximum resonance frequency shifts of 381 MHz for the SHF mode and 3 GHz for the EHF mode, corresponding to fractional frequency changes of 4.5% and 8.3%, respectively. Unlike previously reported ferroelectrically tunable antennas, our reported antenna combines full integration, CMOS compatibility, higher operating frequency, compact footprint, and non-volatile programmability.

  PublisherOA PDFDOI

• Monolithic Integration of a Dual-Mode On-Chip Antenna with a Ferroelectric Hafnium Zirconium Oxide Varactor for Reprogrammable Radio-Frequency Front Ends

  Electronics · 2026-02-12

  articleOpen access

  In this work, we report a dual-mode ferroelectrically programmable on-chip antenna. The antenna is built on a silicon wafer using complementary metal-oxide semiconductor (CMOS) processes and exhibits two programmable resonant modes: one in the super high frequency (SHF) range and one in the extremely high frequency (EHF) range. The SHF mode resonates at 8.5 GHz and exhibits ultrawideband (UWB) behavior, while the EHF mode resonates at 36.6 GHz. Both resonance frequencies can be tuned in a non-volatile fashion by controlling the ferroelectric polarization state of a Hafnium Zirconium Oxide (HZO) varactor monolithically integrated into the feed line. This programmability arises from the ferroelectric switching of the embedded HZO film, which results in a non-volatile variation of its permittivity upon application of a voltage pulse. Ferroelectric switching occurs at approximately ±3 V and induces maximum resonance frequency shifts of 381 MHz for the SHF mode and 3 GHz for the EHF mode, corresponding to fractional frequency changes of 4.5% and 8.3%, respectively. Unlike previously reported ferroelectrically tunable antennas, our reported antenna combines full integration, CMOS compatibility, higher operating frequency, compact footprint, and non-volatile programmability.

  PublisherDOI

• Multi-Material Ink-Jet Printed Microsystems

  2025-12-15

  articleSenior author

  We proposed a novel approach utilizing a multi-material ink-jet additive manufacturing electronics (AME) system to fabricate controllable resistors with tunable sheet resistance and thermal coefficient of resistance properties. We developed a method for achieving tunable electrical properties by mixing conductive and dielectric inks through co-printing and mixing on a substrate. By customizing the AME process parameters and curing sequences, we successfully fabricated high-resistance traces with variations in the plane width of the Conductive Ink (CI) and margins of the uncured Dielectric Ink (DI). These adjustments resulted in sheet resistance ranging from 0.372 to 1.044 Ω/□ at the room temperature, compared to only 0.013–0.033 Ω/□ with the standard printing workflow. This enabled the demonstration of tunability in the temperature coefficient of resistance (TCR), spanning a range of 429-1301 ppm/°C for printed temperature sensors.

  PublisherDOI

• Plasmonically Enhanced Flexural-Mode AlScN Nanoplate Resonator as Uncooled and Ultrafast IR Detector with High Responsivity

  ArXiv.org · 2025-06-26

  preprintOpen access

  This letter introduces a novel class of miniaturized, uncooled, and ultra-fast infrared (IR) resonant thermal detectors (RTDs) based on 30%-doped Aluminum Scandium Nitride (AlScN) nanoplates. Exploiting high electromechanical coupling, good thermal properties, and enhanced and selective IR absorption, the presented device aims to demonstrate significant advancements over the state-of-the-art IR RTDs. This single pixel combines compact footprint, high spectral selectivity and responsivity, reduced noise, and fast thermal response, allowing for the potential development of innovative IR thermal imagers through multi-pixel integration. The flexural nature of the actuated resonance mode eventually enables an interferometric optical readout, paving the way towards achieving extremely low Noise Equivalent Power levels. These results demonstrate a high IR responsivity of around 130 ppt/pW, a thermal time constant of around 330 us, and a large out-of-plane displacement. This work represents the first experimental integration on a resonating platform of plasmonic absorbers that utilize AlScN as dielectric layer.

  PublisherOA PDFDOI

• GHz Ultrasound for Quantitative Oocyte Mechanobiology

  2025-06-29

  articleSenior author

  This paper presents a quantitative characterization method for oocyte mechanobiology that is performed using an AlN piezoMEMS GHz ultrasound transducer array. This is the first demonstration of a GHz ultrasound imaging array (128×128) with high spatial resolution (50×50 μm/pixel) being used for quantitative characterization of a single oocyte cell with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 80\mu \mathrm{m}$</tex> diameter, which provides a pathway to an accurate assessment of mechanical properties for the oocyte selection process in assistive reproductive technologies (ART) [1]. Our ultrasound pulse-echo reflectometry measurements performed at ~1.8GHz using this array showed that we can detect the acoustic impedance changes between 1.89±0.23 MRayl and 1.61±0.28 MRayl depending on the presence and condition of zona pellucida (ZP) to make inferences about oocyte health.

  PublisherDOI

• Highly switchable and reversible soft sticky adhesives based on thermo-responsive phase separation

  Extreme Mechanics Letters · 2025-01-25 · 4 citations

  articleOpen accessCorresponding

  Many biological systems can switch between strong adhering and non-adhering states to various materials with complex shapes and sizes in a reversible manner. By contrast, synthetic soft sticky adhesives, or pressure-sensitive adhesives, still face challenges in combining high switchability, reversible switching, facile switching operation, and applicability to diverse materials, shapes, and sizes. To address this challenge, here we present a highly switchable and reversible soft sticky adhesive based on thermal-induced phase separation in a thermo-responsive hydrogel. At room temperature, the hydrogel adhesive is toughened by nanoclay as noncovalent crosslinkers, showing an adhesion strength of 60–80 kPa to various adherends. This adhesion is almost completely switched off upon heating, with a residual strength of around 1 kPa. The switching is reversible for many cycles, enabling selective pick-and-release of objects with various materials, shapes, sizes, and weights. The switching time is around 10 s with an adhesive layer of 1 mm, governed by thermal conduction through the adhesive, faster than or comparable to most state-of-the-art methods. The adhesive is self-healing, and can be recycled, dried, stored, reswollen, and reused with nearly intact adhesion and switching properties. These features are hoped to advance technologies such as on-demand device disassembly for recycling, assembly-based manufacturing, biomimetic robots, and human-machine interfaces.

  PublisherDOI

• DNN-based Predictive Digital Twin for FHE Manufacturing

  2025-05-05 · 1 citations

  article

  This work presents a novel predictive model and a virtual metrology for Flexible and Printable Electronic (FHE) device fabrication using deep learning techniques. Our method leverages the Deep Neural Network (DNN) based model Pix2Pix to predict outcomes of 3D inkjet printing from layout images. We prepared an experimental FHE dataset that captures printed electronics’s intricate patterns and printing process variability. By training the Pix2Pix model on this dataset, we successfully demonstrated its ability to learn and accurately predict the outcome of inkjet printing of structures using a conductive ink. The best mean absolute errors (MAE) for width and gap prediction are 5.07 microns and 7.99 microns. This approach enables improvement of efficiency and accuracy of printed electronics, paving the way for advancements in FHE manufacturing technology and workflows.

  PublisherDOI

• Evaluation of CMOS Polysilicon Resistors for Thermal Conductivity-Based Gas Sensing

  2025-10-19

  article

  This paper reports the exploration of standard CMOS polysilicon resistors (with and without silicide) for thermal conductivity-based gas sensing. Our approach utilizes standard CMOS layers, with a transient measurement technique based on the thermal time constant (τ). This technique enables robust gas sensing while dramatically reducing sensitivity to drift in the sense resistance. By relying exclusively on resistors already available in a standard CMOS PDK, we present a pathway for scalable and industry-compatible gas sensing solutions.

  PublisherDOI

Frequent coauthors

• Amit Lal

  Cornell University

  79 shared

• Ved Gund

  Cornell University

  28 shared

• Landon Ivy

  Sonic Concepts (United States)

  21 shared

• Visarute Pinrod

  National Nanotechnology Center

  20 shared

• Peter C. Doerschuk

  Cornell University

  20 shared

• Chung Hoon Lee

  Marquette University

  13 shared

• Justin Kuo

  Cornell University

  12 shared

• Di Ni

  11 shared

Labs

• Autonomous Integrated Microsystems (AIMS) LaboratoryPI

Education

• PhD, Electrical and Computer Engineering

  Marquette University

  2015

Awards & honors

• 2026 FLEXI Award for Technology Leadership