Field-effect transistor (FET)-based biosensors have demonstrated highly sensitive label-free detection of a plethora of biomolecules as next-generation binding assays. While the dose–response curve of affinity-based binding assays generally has a nonlinear shape, any distortion contributed by the FET transducers has not been well understood. In this paper, we show that the signal transduction of FET sensors plays an important role in shaping their dose–response curves when operating in the nonlinear screening regime. We have found that the nonlinearity arising from the counterion screening in the electrical double layer could distort the relationship between the device flatband voltage shift and the analyte concentrations in (1) lowering its half-maximal response concentration as well as the sensitive detection range and (2) expanding its dynamic range. Negligence of such nonlinearity would introduce errors in the extracted affinity properties of the analyte–receptor pair. This work provides useful guidelines for designing FET-based binding assays and interpreting their measurement data.

We analyzed the low-frequency noise (LFN) of dual-gated field-effect transistor (DG-FET) biosensors with Schottky contacts. We found the flicker noise at the sensing insulator-semiconductor interface to be the major noise source while employing Schottky contacts to have minimal noise contribution with a sufficiently large back-gate bias voltage. The measured noise dependence on transconductance further indicated the presence of a nonuniform energy distribution of interface trap density at the said sensing interface. Based on these findings, we argued that the DG structure is advantageous over its single-gated (SG) counterpart; although they possess the same intrinsic lower limit of detection (LLOD), the former could offer a larger signal gain at the optimum LLOD thanks to sufficient channel carrier supply through back-gating instead of biasing the sensing interface toward band edge with higher trap density.

Ultra-long metal nanowires and their facile fabrication have been long sought after as they promise to offer substantial improvements of performance in numerous applications. However, ultra-long metal ultrafine/nanowires are beyond the capability of current manufacturing techniques, which impose limitations on their size and aspect ratio. Here we show that the limitations imposed by fluid instabilities with thermally drawn nanowires can be alleviated by adding tungsten carbide nanoparticles to the metal core to arrive at wire lengths more than 30 cm with diameters as low as 170 nm. The nanoparticles support thermal drawing in two ways, by increasing the viscosity of the metal and lowering the interfacial energy between the boron silicate and zinc phase. This mechanism of suppressing fluid instability by nanoparticles not only enables a scalable production of ultralong metal nanowires, but also serves for widespread applications in other fluid-related fields.

Electrical properties of biological cells and tissues possess valuable information that enabled numerous applications in biomedical engineering. The common foundation behind them is a numerical model that can predict electrical response of a single cell or a network of cells. We analyzed the past empirical observations to propose the first statistical model that accurately mimics biological diversity among animal cells, yeast cells, and bacteria. Based on membrane elasticity and cell migration mechanisms, we introduce a more realistic three-dimensional geometry generation procedure that captures membrane protrusions and retractions in adherent cells. Together, they form a model of diverse electrical response across multiple cell types. We experimentally verified the model with electrical impedance spectroscopy of a single human cervical carcinoma (HeLa) cell on a microelectrode array. The work is of particular relevance to medical diagnostic and therapeutic applications that involve exposure to electric and magnetic fields.

In this work, we propose a wide-band frequency reconfigurable patch antenna with switchable slots (PASS) based on liquid metal manipulation in 3D printed microfluidic channel. The antenna operation relies on continuous movement of the liquid metal volume over the channel that covers the antenna slots. The patch antenna was designed with three pairs of composite slots, with the microfluidic channel bonded on top of the metal layer. Simulation results shows that by tuning and switching the liquid metal loaded slot in the designed microfluidic channel, a frequency tuning bandwidth of around 70% is achieved, without significant changing in the radiation pattern and radiation efficiency. Prototype has been fabricated and preliminary testing shows good agreement between simulations and measurements.

Electric cell-substrate impedance sensing (ECIS) has been instrumental in tracking collective behavior of confluent cell layers for decades. Toward probing cellular heterogeneity in a population, the single-cell version of ECIS has also been explored, yet its intrinsic capability and limitation remain unclear. In this work, we argue for the fundamental feasibility of impedance spectroscopy to track changes of multiple cellular properties using a noninvasive single-cell approach. While changing individual properties is experimentally prohibitive, we take a simulation approach instead and mimic the corresponding changes using a 3D computational model. From the resultant impedance spectra, we identify the spectroscopic signature characteristic to each property considered herein. Since multiple properties change concurrently in practice, the respective signatures often overlap spectroscopically and become hidden. We further attempt to deconvolve such spectra and reveal the underlying property changes. This work provides the theoretical foundation to inspire experimental validation and adoption of ECIS for multiproperty single-cell measurements.

Peristaltic pumping has been widely adopted in microfluidic systems over the past decade. Most applications lie, however, in the regime where fluids or biospecimens are continuously pumped through the entire fluidic system, leaving the initial filling stage of fluid into an air-filled channel underexplored. We propose a compact, quasi-1D, lumped element model for describing the initial filling process of liquid into microfluidic channels driven by peristaltic pumps with discrete diaphragm valves. In addition, we experimentally demonstrated that the liquid penetration length into the fluid channel could be decently controlled (~ 0.3 mm/cycle) using human fingers as the source of actuation pressures. Moreover, we show from our experiments the possibility of controlling the profiles liquid penetration lengths to be other than Lucas–Washburn’s \( l\,\sim\,t^{{\frac{1}{2}}} \) dependence: Linear liquid penetration length profiles can be achieved via liquid channels with exponentially decaying cross-sectional areas. In the above ways, our model has successfully demonstrated that finger-operated peristaltic pumps can serve as an upgraded alternative for capillary-driven microfluidics, offering extra runtime adjustment capability in understanding and engineering of microfluidics in the initial filling stages.

A common setback to electron transport models for quantum cascade laser active regions is the inability to freely simulate widely varying designs. One solution to this problem is to use a density matrix formalism with a generalized treatment of scattering, wherein the well-defined energy eigenbasis is used, and the relative simplicity of the density matrix can be taken advantage of for rapid simulations. We have developed such a model from first principles in the past, and now built a simulator for terahertz quantum cascade lasers that calculates a fully self-consistent solution to the coupled problem of bandstructure, lasing field strength, and space charge. This level of depth enables us to examine the model's performance across much of the design space and operating temperatures, for which we find generally good agreement. Areas for future improvement of the model are discussed, particularly the treatment of electron-electron scattering and continuum leakage. The model also enables us to make qualitative insights into the microscopic workings of the active regions, such as the nonequilibrium subband distributions and their response to the optical field, and the possibility for using two sequential optical transitions.

We propose, demonstrate, and assess a nontunneling-based nMOS voltage-controlled negative differential resistance (V-NDR) concept for overcoming the intrinsic efficiency and reliability shortcomings of magnetic random access memory memories (MRAM). Using nMOS V-NDR circuits in series with MRAM tunnel junctions, we experimentally observe 40 times reduction in current during switching, enabling write termination and read margin amplification. Large scale Monte Carlo simulations also show 5X improvement in write energy savings and demonstrate the robustness of the scheme against device variability.

We derive a density matrix (DM) theory for quantum cascade lasers (QCLs) that describes the influence of scattering on coherences through a generalized scattering superoperator. The theory enables quantitative modeling of QCLs, including localization and tunneling effects, using the well-defined energy eigenstates rather than the ad hoc localized basis states required by most previous DM models. Our microscopic approach to scattering also eliminates the need for phenomenological transition or dephasing rates. We discuss the physical interpretation and numerical implementation of the theory, presenting sets of both energy-resolved and thermally averaged equations which can be used for detailed or compact device modeling. We illustrate the theory's applications by simulating a high performance resonant-phonon terahertz (THz) QCL design which cannot be easily or accurately modeled using conventional DM methods. We show that the theory's inclusion of coherences is crucial for describing localization and tunneling effects consistent with experiment.

Evaluation of novel devices in a circuit context is crucial to identifying and maximizing their value. We propose a new framework, PROCEED, and metrics for accurate device-circuit co-evaluation through proper optimization of digital circuit benchmarks. PROCEED assesses technology suitability over a wide operating region (MHz to GHz) by leveraging available circuit knobs (Vt assignment, power management, sizing, etc.) and improves accuracy by 3X to 115X compared to existing methods while offering orders of magnitude improvements in runtime over full physical design implementation flows. To illustrate PROCEED's capabilities, we deploy it to assess novel tunneling transistors (TFETs) compared to conventional CMOS.

We have studied the electrostatics of nanowire bioFETs as their channel widths vary. It is commonly believed that smaller bioFET channel widths and heights result in better sensitivity, which is attributed to increased surface-area-to-volume ratios of the bioFET channels. The simulations and analytical arguments presented here show that this reasoning is flawed. Instead, our work suggests that the local curvature of the bioFET surface, especially the corners, affects the electrostatic potential that results from the captured target analytes. Nanowires of any width have the same number of corners, but as the nanowire width shrinks, the beneficial concave corners contain a larger fraction of the total surface area, and their relative importance increases, leading to a slight increase in the bioFET signal.

The adoption of spin-transfer torque random access memory (STT-RAM) into nonvolatile memory systems faces three major obstacles: high write energy, low sensing margin, and high read disturbance. Many designs have been suggested to resolve each of these challenges separately and at the cost of significant overhead. We propose a single low-overhead solution to all these problems without changing the underlying memory architecture by using negative differential resistance devices like tunnel diodes or tunnel field-effect transistors to assist the STT-RAM write and read process. We show through simulations that the proposed designs can dramatically improve the write and read energy efficiency and sensing margins while minimizing the read disturbance, even after accounting for process variations. Our results open a design path for energy-efficient and reliable STT-RAM technologies.

The commercialization of new point of care technologies holds great potential in facilitating and advancing precision medicine in heart, lung, blood, and sleep (HLBS) disorders. The delivery of individually tailored health care to a patient depends on how well that patient's health condition can be interrogated and monitored. Point of care technologies may enable access to rapid and cost-effective interrogation of a patient's health condition in near real time. Currently, physiological data are largely limited to single-time-point collection at the hospital or clinic, whereas critical information on some conditions must be collected in the home, when symptoms occur, or at regular intervals over time. A variety of HLBS disorders are highly dependent on transient variables, such as patient activity level, environment, time of day, and so on. Consequently, the National Heart Lung and Blood Institute sponsored a request for applications to support the development and commercialization of novel point-of-care technologies through small businesses (RFA-HL-14-011 and RFA-HL-14-017). Three of the supported research projects are described to highlight particular point-of-care needs for HLBS disorders and the breadth of emerging technologies. While significant obstacles remain to the commercialization of such technologies, these advancements will be required to achieve precision medicine.

We develop an evaluation framework to assess the potential benefits of feature-level heterogeneous integration (HGI) in nanoscale VLSI circuits. We study, for the first time, the impact of HGI on circuit delay, layout area, and power by comparing the integration of 15-nm InGaAs and Ge FinFETs via nanotransfer printing with the baseline Si-only FinFET technology. To properly account for the performance, power, and area tradeoffs, we perform comprehensive evaluations, including synthesis, placement, and routing of digital circuit benchmarks. We show the circuits designed with an HGI exhibit lower delay and power due to improved device performance at the cost of larger area induced by misalignment errors. We also demonstrate that the HGI misalignment area penalties can be drastically reduced using posttransfer fin trimming. Our findings provide substantial motivation for industry to explore HGI as a technology route for the post-Si era.

Optoelectronic tweezers (OET) has advanced within the past decade to become a promising tool for cell and microparticle manipulation. Its incompatibility with high conductivity media and limited throughput remain two major technical challenges. Here a novel manipulation concept and corresponding platform called Self-Locking Optoelectronic Tweezers (SLOT) are proposed and demonstrated to tackle these challenges concurrently. The SLOT platform comprises a periodic array of optically tunable phototransistor traps above which randomly dispersed single cells and microparticles are self-aligned to and retained without light illumination. Light beam illumination on a phototransistor turns off the trap and releases the trapped cell, which is then transported downstream via a background flow. The cell trapping and releasing functions in SLOT are decoupled, which is a unique feature that enables SLOT's stepper-mode function to overcome the small field-of-view issue that all prior OET technologies encountered in manipulation with single-cell resolution across a large area. Massively parallel trapping of more than 100,000 microparticles has been demonstrated in high conductivity media. Even larger scale trapping and manipulation can be achieved by linearly scaling up the number of phototransistors and device area. Cells after manipulation on the SLOT platform maintain high cell viability and normal multi-day divisibility.

While animal experimentations have spearheaded numerous breakthroughs in biomedicine, they also have spawned many logistical concerns in providing toxicity screening for copious new materials. Their prioritization is premised on performing cellular-level screening in vitro. Among the screening assays, secretomic assay with high sensitivity, analytical throughput, and simplicity is of prime importance. Here, we build on the over 3-decade-long progress on transistor biosensing and develop the holistic assay platform and procedure called semiconductor electronic label-free assay (SELFA). We demonstrate that SELFA, which incorporates an amplifying nanowire field-effect transistor biosensor, is able to offer superior sensitivity, similar selectivity, and shorter turnaround time compared to standard enzyme-linked immunosorbent assay (ELISA). We deploy SELFA secretomics to predict the inflammatory potential of eleven engineered nanomaterials in vitro, and validate the results with confocal microscopy in vitro and confirmatory animal experiment in vivo. This work provides a foundation for high-sensitivity label-free assay utility in predictive toxicology.

We report a novel Self-Locking Optoelectronic Tweezers (SLOT) for single-cell manipulation in cell culture media across a large area. SLOT overcomes two major technical barriers of conventional optoelectronic tweezers (OET) toward high throughput single-cell manipulation. Its unique lateral, ring-shaped phototransistor design enables manipulation in high conductivity media (1 S/m) and overcomes a fundamental blurry optical pattern issue for single-cell manipulation in large area (> 1 cm2).