Models for the use of commercial TCAD in the analysis of silicon-based integrated biosensors

We present a simple approach to describe electrolytes in TCAD simulators for the modeling of nano-biosensors. The method exploits the similarity between the transport equations for electrons and holes in semiconductors and the ones for charged ions in a solution. We describe a few workarounds to improve the model accuracy in spite of the limitations of commercial TCAD. Applications to the simulations of silicon nanowire and nanoelectrode biosensors are reported as relevant examples

A retrospective epidemiological study on the association of bullous pemphigoid and neurological diseases

Bullous pemphigoid is a rare chronic recurrent dermatosis that is often reported in association with various neurological diseases. No investigation involving a large number of patients has ever been carried out to demonstrate such an association. This study was accomplished by analysing the discharge diagnosis of all hospitalized patients, both day-patients and inpatients, during a 5-year period (1995-2000) covering a total population group of 934,023 living in a region of Italy that has approximately 1,200,000 inhabitants. The results support the hypothesis of an association between bullous pemphigoid, multiple sclerosis and Parkinson's disease on a highly significant statistical basis. The aetiopathogenic mechanisms and the causes that induce the loss of immunological tolerance are not yet understood

Technological development of high-k dielectric FinFETs for liquid environment

This work presents the technological development and characterization of n-channel fully depleted high-k dielectric FinFETs (Fin Field Effect Transistor) for applications in a liquid environment. Herein, we provide a systematic approach based on Finite Element Analysis for a high-control fabrication process of vertical Si-fins on bulk and we provide many useful fabrication expedients. Metal gate FinFETs have been successfully electrically characterized, showing excellent subthreshold slope SS = 72 mV/dec and high Ion/Ioff

Unified framework for a side-by-side comparison of different multicomponent algorithms : Lattice Boltzmann vs. phase field model

Lattice Boltzmann models (LBM) and phase field models (PFM) are two of the most widespread approaches for the numerical study of multicomponent fluid systems. Both methods have been successfully employed by several authors but, despite their popularity, still remains unclear how to properly compare them and how they perform on the same problem. Here we present a unified framework for the direct (one-to-one) comparison of the multicomponent LBM against the PFM. We provide analytical guidelines on how to compare the Shan–Chen (SC) lattice Boltzmann model for non-ideal multicomponent fluids with a corresponding free energy (FE) lattice Boltzmann model. Then, in order to properly compare the LBM vs. the PFM, we propose a new formulation for the free energy of the Cahn–Hilliard/Navier–Stokes equations. Finally, the LBM model is numerically compared with the corresponding phase field model solved by means of a pseudo-spectral algorithm. This work constitute a first attempt to set the basis for a quantitative comparison between different algorithms for multicomponent fluids. We limit our scope to the few of the most common variants of the two most widespread methodologies, namely the lattice Boltzmann model (SC and FE variants) and the phase field model

Characterization and modelling of differential sensitivity of nanoribbon-based pH-sensors

We report accurate characterization, modelling and simulation of SOI nanoribbon-based pH sensors and we compare operation in air (dry) and electrolyte (wet) environments. We find remarkably different current density distributions and geometry scaling rules, but similar series resistances and active trap state densities in the two configurations. Calibrated TCAD based simulations implementing an original approach to model the site-binding harge, and in good agreement with experiments, provide the necessary insights to interpret the non trivial dependence of the threshold voltage and current sensitivity on pH

Impact of different receptor binding modes on surface morphology and electrochemical properties of PNA-based sensing platforms.

Silicon-based field-effect devices have been widely studied for label-free DNA detection in recent years. These devices rely on the detection of changes in the electrical surface potential during the DNA recognition event and thus require a reliable and selective immobilization of charged biomolecules on the device surface [1]. The preparation of self-assembled monolayers of phosphonic acids (SAMPs) on metal oxide surfaces is an efficient approach to generate well-defined organic interfaces with a high density of receptor binding sites close to the sensing surface [2,3]. In this work, we report the functionalization and characterization of silicon/silicon nitride surfaces with different types of peptide nucleic acid (PNA), a synthetic analogue to DNA [4]. Differently modified PNA molecules are covalently immobilized on the underlying SAMPs either in a multidentate or monodentate fashion to investigate the effect of different binding modes on receptor density and morphology important for PNA-DNA hybridization (Scheme 1). Multidentate immobilization of the bioreceptors via C6-SH attachment groups at the γ-points along the PNA backbone provides a rigid, lying configuration on the device surface (PNA 1), whereas a monodentate immobilization by Cys-capped PNA molecules (PNA 2) results in more flexible and more accessible receptor binding sites. Our results indicate that the presented functionalization scheme can be successfully applied to produce morphologically and electrochemically different PNA bioreceptor binding sites on silicon/silicon nitride surfaces. Consequently, a well-chosen modification of the PNA backbone is a valid approach to influence the sensing properties of surface-immobilized PNA bioreceptors, which might provide an additional parameter to further tune and tailor the sensing capabilities of PNA-based biosensing devices

Bayesian estimation of physical and geometrical parameters for nanocapacitor array biosensors

8siMassively parallel nanosensor arrays fabricated with low-cost CMOS technology represent powerful platforms for biosensing in the Internet-of-Things (IoT) and Internet-of-Health (IoH) era. They can efficiently acquire “big data” sets of dependable calibrated measure-ments, representing a solid basis for statistical analysis and parameter estimation. In this paper we propose Bayesian estimation methods to extract physical parameters and interpret the statistical variability in the measured outputs of a dense nanocapacitor array biosensor. Firstly, the physical and mathematical models are presented. Then, a simple 1D-symmetry structure is used as a validation test case where the estimated parameters are also known a-priori. Finally, we apply the methodology to the simultaneous extraction of multiple physical and geometrical parameters from measurements on a CMOS pixelated nanocapacitor biosensor platform.reservedmixedStadlbauer, Benjamin; Cossettini, Andrea; Morales E., José A.; Pasterk, Daniel; Scarbolo, Paolo; Taghizadeh, Leila; Heitzinger, Clemens; Selmi, LucaStadlbauer, Benjamin; Cossettini, Andrea; Morales E., José A.; Pasterk, Daniel; Scarbolo, Paolo; Taghizadeh, Leila; Heitzinger, Clemens; Selmi, Luc