An Extended CMOS ISFET Model Incorporating the Physical Design Geometry and the Effects on Performance and Offset Variation

This paper presents an extended model for the CMOS-based ion-sensitive field-effect transistor, incorporating design parameters associated with the physical geometry of the device. This can, for the first time, provide a good match between calculated and measured characteristics by taking into account the effects of nonidealities such as threshold voltage variation and sensor noise. The model is evaluated through a number of devices with varying design parameters (chemical sensing area and MOSFET dimensions) fabricated in a commercially available 0.35-µm CMOS technology. Threshold voltage, subthreshold slope, chemical sensitivity, drift, and noise were measured and compared with the simulated results. The first- and second-order effects are analyzed in detail, and it is shown that the sensors' performance was in agreement with the proposed model

Validity of the parabolic effective mass approximation in silicon and germanium n-MOSFETs with different crystal orientations

This paper investigates the validity of the parabolic effective mass approximation (EMA), which is almost universally used to describe the size and bias-induced quantization in n-MOSFETs. In particular, we compare the EMA results with a full-band quantization approach based on the linear combination of bulk bands (LCBB) and study the most relevant quantities for the modeling of the mobility and of the on-current of the devices, namely, the minima of the 2-D subbands, the transport masses, and the electron density of states. Our study deals with both silicon and germanium n-MOSFETs with different crystal orientations and shows that, in most cases, the validity of the EMA is quite satisfactory. The LCBB approach is then used to calculate the values of the effective masses that help improve the EMA accuracy. There are crystal orientations, however, where the 2-D energy dispersion obtained by the LCBB method exhibits features that are difficult to reproduce with the EMA model

Accurate simulations of the interplay between process and statistical variability for nanoscale FinFET-based SRAM cell stability

In this paper we illustrate how by using advanced atomistic TCAD tools the interplay between long-range process variation and short-range statistical variability in FinFETs can be accurately modelled and simulated for the purposes of Design-Technology Co-Optimization (DTCO). The proposed statistical simulation and compact modelling methodology is demonstrated via a comprehensive evaluation of the impact of FinFET variability on SRAM cell stability