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A methodology to build multi-compartment lumped elements equivalent circuits for the neuron/electrode systems is proposed. The equivalent circuit topology is derived by careful scrutiny of accurate multiphysics finite-elements method (FEM) simulations that couple ion transport in the intra- and extracellular fluids, activation of ion channels in the cellular membrane, and signal collection by the electronic readout, thus improving upon most common area contact models. We show that the equivalent circuits derived with our method match with good accuracy the reference FEM simulations over a wide range of geometrical/physical parameters such as the neuron and electrode size, the thickness of the electrolytic cleft, the input impedance of the readout amplifier, even in presence of nonuniform ion channel distributions. The impact of the number of compartments on the model accuracy is also analyzed in detail. We finally illustrate by FEM simulations the effect of extracellular ion transport on the reversal potentials of the Hodgkin-Huxley neuron model and how it can affect the recorded signal for very thin electrolyte clefts between the neuron and the electrode, in a way not yet captured by equivalent circuits of the neuron/electrode system.

We derive a complete set of expressions for the MOSFET gate and drain power spectral densities due to elastic and inelastic trapping/detrapping of channel carriers into the gate dielectric. Our calculations explain trapping/detrapping noise (TDN) in various FET operating regions and highlight trap’s position-dependent terms, often neglected in the literature, which are instead important for devices with thin gate dielectrics. Furthermore, we show that TDN has a contribution to the gate current noise, correlated with the drain current fluctuations and we highlight the role of the transfer function between channel charge fluctuations and drain current on the noise characteristics. The model expressions are carefully validated by comparison with 2-D and 3-D TCAD simulations of scaled MOSFETs with different architectures (bulk, fully depleted-silicon-on-insulator (FD-SOI), FinFET), channel, and gate materials. Besides shedding new light on TDN, the results could enable trap density extraction from experimental samples with improved accuracy and pave the way to complete and accurate compact models for TDN in MOSFETs.

We present and validate an equilibrium model based on the Poisson-Boltzmann equations that includes the main ingredients to simulate ion-sensitive membranes in the context of electrochemical potentiometric sensors with and without ionophores. With respect to phase boundary models, our model includes spatial resolution of the electrostatic potential and concentrations. The model enables the study of Nernstian and non-Nernstian equilibrium responses, helps improving the detection range and investigating selectivity and cross-sensitivity issues related to interferent ions in the sample solution. Therefore, the model is a useful support for the design of potentiometric microelectronic sensors and helps optimizing relevant membrane features such as ionic sites and ionophore concentration for best sensitivity and selectivity.

Neuron and neural network studies are remarkably fostered by novel stimulation and recording systems, which often make use of biochips fabricated with advanced electronic technologies and, notably, micro- and nanoscale complementary metal-oxide semiconductor (CMOS). Models of the transduction mechanisms involved in the sensor and recording of the neuron activity are useful to optimize the sensing device architecture and its coupling to the readout circuits, as well as to interpret the measured data. Starting with an overview of recently published integrated active and passive micro/nanoelectrode sensing devices for

We present new experimental evidence and extensive numerical simulations of a few distinct fingerprints generated by dielectric and conductive microparticles in electrolyte environment on the capacitance spectra of nanoelectrode array sensors. Finite element simulations in good agreement with measurements allow us to identify unambiguously the physical origin of these features, and to illustrate their dependence on the system’s geometrical and physical properties. In particular, we show that conductive particles induce a response with complex space and frequency dependencies, caused by the formation of an AC electrical double layer at the particle surface, and its interaction with the working and counter electrodes in the array. Furthermore, we highlight features that could lead to false-negative detection events in sensing applications. The theoretical predictions are confirmed by experiments on a state of the art CMOS pixelated nanocapacitor biosensor platform.

We present an approach to simulate ion’s driftand diffusion in chemical sensors based on ion-selective-membranes (ISMs) in either ion-selective electrode structuresor coupled to field-effect transistors. The model is used to analyze the sensitivity, selectivity, and transient responseof ISMs in non-equilibrium conditions upon real-time concentration changes. Our simple implementation combinesadvanced features from semiconductor theory and analytical electrochemistry, such as the Schafetter-Gummel discretizationscheme and Chang-Jaffé boundary conditions for ions at the interfaces, thus allowing to perform simulations beyond sensing. The results are in agreement with experimental transient responses reported in the literature. As relevant case studies, we examine the ISM preconditioning in miniaturized sensors and the electrostatic interaction between the FET channel and ISMs. In the first case, simulations reveal that calibration curves performed on incomplete ISM conditioning can lead to hysteretical responses when the ion affinity in the electrolyte and ISM is similar (e.g., with organic ions). In the second case, we find that the gate oxide field in contact with the ISM affects the device characteristics such that the ion concentrations not only change the FET threshold voltage but also the slope of its IV curve. This effect can be minimized by working in the subthreshold regime or using extended gates.

Neuron and neural network studies are remarkably fostered by novel stimulation and recording systems, which often make use of biochips fabricated with advanced electronic technologies and, notably, micro- and nanoscale complementary metal-oxide semiconductor (CMOS). Models of the transduction mechanisms involved in the sensor and recording of the neuron activity are useful to optimize the sensing device architecture and its coupling to the readout circuits, as well as to interpret the measured data. Starting with an overview of recently published integrated active and passive micro/nanoelectrode sensing devices for in vitro studies fabricated with modern (CMOS-based) micro-nano technology, this paper presents a mixed-mode device-circuit numerical-analytical multiscale and multiphysics simulation methodology to describe the neuron-sensor coupling, suitable to derive useful design guidelines. A few representative structures and coupling conditions are analysed in more detail in terms of the most relevant electrical figures of merit including signal-to-noise ratio. This article is part of the theme issue ‘Advanced neurotechnologies: translating innovation for health and well-being’.

In this work, we exploit the general-purpose solver COMSOL, equipped with electrolyte and semiconductor physics modules, to implement a versatile model of potentiometric chemical sensors including arbitrarily complex surface reactions at the oxide/electrolyte interface with examples on 2D device-level simulations of an ISFET. Firstly, Multiphysics simulations of V TH sensitivity to pH sensing are compared with analyses based on semiconductor TCAD. Then, more complex Na+sensing experiments are examined and numerical simulations are compared against 1D electrochemical models.

CMOS-based nanocapacitor arrays allow locally probing the impedance of an electrolyte in real time and with sub-micron spatial resolution. Here we report on the physico-chemical characterization of individual microdroplets of oil in a continuous water phase using this new tool. We monitor the sedimentation and wetting dynamics of individual droplets, estimate their volume and infer their composition based on their dielectric constant. From measurements before and after wetting of the surface, we also attempt to estimate the contact angle of individual micron-sized droplets. These measurements illustrate the capabilities and versatility of nanocapacitor array technology., Faraday Discussions, 233, ISSN:1359-6640, ISSN:1364-5498

We propose a simple expression to relate the total excess noise factor of a single-carrier multiplication staircase avalanche photodiode (APD) to the excess noise factor and gain given by the individual conduction band discontinuities. The formula is valid when electron impact ionization dominates hole impact ionization; hence, it is especially suited for staircase APDs with In-rich multiplication regions, as opposed, for example, to GaAs/AlGaAs systems where hole ionization plays an important role. The formula has been verified by accurate means of numerical simulations based on a newly developed nonlocal history dependent impact ionization model.

We describe the multi-valley/multi-subband Monte Carlo (MV–MSMC) approach to model nanoscale MOSFETs featuring III–V semiconductors as channel material. This approach describes carrier quantization normal to the channel direction, solving the Schrödinger equation while off-equilibrium transport is captured by the multi-valley/multi-subband Boltzmann transport equation. In this paper, we outline a methodology to include quantum effects along the transport direction (namely, source-to-drain tunneling) and provide model verification by comparison with Non-Equilibrium Green’s Function results for nanoscale MOSFETs with InAs and InGaAs channels. It is then shown how to use the MV–MSMC to calibrate a Technology Computer Aided Design (TCAD) simulation deck based on the drift–diffusion model that allows much faster simulations and opens the doors to variability studies in III–V channel MOSFETs.

In this article, we separately investigate the role of electric field and device self-heating (SHE) in enhancing the charge emission process from Fe-related buffer traps (0.52 eV from ${E}_{c}$ ) in AlGaN/GaN high electron mobility transistors (HEMTs). The experimental analysis was performed by means of drain current transient (DCT) measurements for either: 1) different dissipated power ( ${P}_{D,\mathrm {steady}}$ ) at constant drain-to-source bias ( ${V}_{\mathrm {DS,steady}}$ ) or 2) constant ${P}_{D,\mathrm {steady}}$ at different ${V}_{\mathrm {DS,steady}}$ ’s. We found that: 1) an increase in ${P}_{D,\mathrm {steady}}$ yields an acceleration in the thermally activated emission process, consistently with the temperature rise induced by SHE and 2) on the other hand, the field-effect turned out to be negligible within the investigated voltage range, indicating the absence of the Poole–Frenkel effect (PFE). A qualitative analysis based on the electric field values obtained by numerical simulations is then presented to support the interpretation and conclusions.

We present a Full-Band Monte Carlo (FBMC) investigation of impact ionization in GaAs p-i-n Avalanche Photodiodes (APDs). FBMC simulations have been used to compute the gain and the excess noise factor and a new equation has been derived for the extraction of history-dependent impact ionization coefficients from FBMC simulations. Results from FBMC are then compared with the ones of nonlocal historydependent impact ionization models. We found that at high reverse bias voltages it is important to take into account the fact that secondary carriers are generated with nonzero kinetic energy. Finally, we propose an improved history-dependent model using an energy-dependent mean free path.

We propose a steady state model for ion selective membranes (ISM) as selectivity element in potentiometric sensors. The model solves the Poisson-Boltzmann equation, coupled to distributed chemical reactions between ionophores and two types of competing ions. We show that a Donnan potential arises when ionic sites are present, while selectivity is achieved only if using ionophore-doped ISMs. The model allows to evaluate cross-sensitivities and can explain steady state non-Nernstian responses. We also provide an application example of sensor parameter design supported by the proposed model.

This paper reports a design-oriented numerical study of vertical Si-nanowires to be used as sensing elements for the detection of the intracellular electrical activity of neurons. An equivalent lumped-element circuit model is derived and validated by comparison with physics-based numerical simulations. Most of the component values can be identified individually by geo- metrical and physical considerations. The transfer function and the SNR of the sensor in presence of thermal noise are derived, and the impact of the device geometry is shown.

We derive an analytical model for 1/f noise in MOSFETs, highlighting a term that is often neglected in literature but becomes important for ultra-thin oxides. Furthermore, we identify an interesting relationship between the thermal noise of the gate impedance and the gate noise due to trapping/detrapping between the free carriers in the channel and the oxide traps, as well as the 1/f noise cross-correlation between drain and gate, showing that a single voltage noise generator is not enough to describe completely the 1/f noise. TCAD simulations are used to verify the model predictive capabilities.

The accurate extraction and the reliable, repeatable reduction of graphene - metal contact resistance (R$_{C}$) are still open issues in graphene technology. Here, we demonstrate the importance of following clear protocols when extracting R$_{C}$ using the transfer length method (TLM). We use the example of back-gated graphene TLM structures with nickel contacts, a complementary metal oxide semiconductor compatible metal. The accurate extraction of R$_{C}$ is significantly affected by generally observable Dirac voltage shifts with increasing channel lengths in ambient conditions. R$_{C}$ is generally a function of the carrier density in graphene. Hence, the position of the Fermi level and the gate voltage impact the extraction of R$_{C}$. Measurements in high vacuum, on the other hand, result in dependable extraction of R$_{C}$ as a function of gate voltage owing to minimal spread in Dirac voltages. We further assess the accurate measurement and extraction of important parameters like contact-end resistance, transfer length, sheet resistance of graphene under the metal contact and specific contact resistivity as a function of the back-gate voltage. The presented methodology has also been applied to devices with gold and copper contacts, with similar conclusions., Comment: 23 pages

This work demonstrates that when inelastic tunneling between oxide traps and semiconductor bands is considered, the traps with energy aligned to the semiconductor bandgap play a significant role in the frequency dispersion of the capacitance–voltage ( C–V ) and conductance–voltage ( G–V ) characteristics of metal–oxide–semiconductor (MOS) systems. The work also highlights that a nonlocal model for tunneling into interface states is mandatory to reproduce experiments when carrier quantization in the inversion layer is accounted for. A model, including these ingredients, is used to evaluate the energy and depth distribution of oxide traps in a n-In 0.53 Ga 0.47 As/Al 2 O 3 MOS system and is able to accurately fit the C–V frequency dispersion from depletion to weak inversion. The oxide trap distribution determined from the C–V response predicts the corresponding G–V dispersion with frequency.

A design-oriented numerical study of vertical Si-nanowires to be used as sensing elements for the detection of the intracellular electrical activity of neurons. An equivalent lumped-element circuit model is derived and validated by comparison with physics-based numerical simulations. Most of the component values can be identified individually by geometrical and physical considerations. The transfer function and the SNR of the sensor in presence of thermal noise are derived, and the impact of the device geometry is shown.

A recently developed nonlocal history dependent model for electron and hole impact ionization is used to compute the gain and the excess noise factor in avalanche photodiodes featuring heterojunctions of III-V compound semiconductors while accounting for both carriers. The model has been calibrated with measurements by our group, as well as on noise versus gain data from the literature. We explore the avalanche photodiode design trade-offs related to the number of GaAs/AlGaAs conduction band steps for X-ray spectroscopy applications.

Extensive experimental characterization and TCAD simulation analysis have been used to study the dark current in Avalanche Photo-Diodes (APDs). The comparison between the temperature dependence of measurements and simulations points out that SRH generation/recombination is responsible for the observed dark current. After the extraction of the carrier lifetimes in the GaAs layers, they have been used to predict the APD collection efficiency of the photo-generated currents under realistic operation conditions and as a function of the photogeneration position inside the absorption layer.

This work focuses on avalanche photodiodes based on GaAs/AlGaAs with separated absorption and multiplication regions (SAM-APDs). The two regions are separated by a thin p-doped layer which, under the application of a reverse bias, is able to confine the potential drop only in the multiplication region. We realized such layer under the form of either a delta sheet of C atoms or a 50-nm-thick GaAs:C layer. Devices with these two structures will be discussed and compared in terms of capacitance and response to light.