Towards Sensing Protein Interactions and Dynamics via 1/f Noise in Graphene Field-Effect Transistors

Deep level transient spectroscopy (DLTS) is widely spread characterization method of traps in semiconductor material, however, it is hardly applicable to devices with a high leakage current. Due to this reason, DLTS results for narrow-band semiconductor devices are still lacking. In many cases, low-frequency noise spectroscopy (LFNS) can overcome the limitations of DLTS, because current fluctuations instead of the transient capacitance are analyzed. In the paper, the duality and complementarity of deep level transient spectroscopy and low-frequency noise spectroscopy is demonstrated and discussed for InAs/GaSb superlattice mid-wavelength IR detector with unipolar bulk barrier. The trap activation energy E A = 300 meV is obtained with both techniques; however, LFNS detects a few other trap activation energies, namely E A = 240 meV, 180 meV, and 130 meV, while the DLTS found also E A = 490 meV.

This paper brings a comparison of the traps identified in triple-gate FinFETS and Gate-All-Around (GAA) nanowire (NW) MOSFETs built with the same technological process. Traps have been identified using low frequency noise (LFN) spectroscopy, giving information on which process steps may be improved in order to build better devices.

Low-frequency electric noise spectroscopy in different polymer/carbon nanotubes composites

We investigate the variability of a ferroelectric FET (FEFET) in program operation using low-frequency noise (LFN) spectroscopy. Contrary to the previous report, LFN characteristics of FEFETs differ significantly depending on the program [low threshold voltage (Vth)] or erase state [high Vth)] [Shin et al., IEEE Electron Device Lett. 43, 13 (2022)]. Furthermore, the 1/f noise variation of the FEFETs is much larger in the program state than that in the erase state. It is revealed that the change in the number of electrons trapped at the FE/dielectric interface and oxygen vacancy in each program operation is the main reason for the variability of the FEFET in program operation. The variation stemming from the change in the number of trapped charges is significantly worsened when the channel area is scaled down.

We have used low-frequency noise (LFN) spectroscopy to characterize generation-recombination (G-R) centers in silicon nanowires grown using chemical vapor deposition. The LFN spectra showed Lorentzian behavior with well-defined corner-frequency indicative of single G-R center in the bandgap. From the temperature-dependent LFN measurement a single deep level at 0.39 eV from the bandedge is identified, which matches closely with the Au donor level in Si. The trap concentration was estimated at 2.0 × 1012 cm−3 with electron and hole capture cross-sections of 9.5 × 10−17 cm2 and 1.4 × 10−16 cm2, respectively. This study demonstrates the potential of the LFN spectroscopy in characterization of deep-levels in nanowires.

Anti-ambipolar transistors (AATs) featuring heterojunctions of n- and p-type semiconductors have garnered significant research interest owing to their unique electrical characteristics. With the nonlinear current response, AATs hold great promise for a wide range of next-generation electronic applications, further enhancing advanced logic and in-memory computing functionality. However, the seamless integration of AATs into these applications hinges upon addressing their susceptibility to temperature and bias instabilities, a challenge that has yet to be systematically explored. Here, the origin of these instabilities is reported in AATs composed of indium-gallium-zinc oxide (IGZO) and dinaphtho[2,3-b:2',3'-fjthieno[3,2-b]thiophene (DNTT) through low-frequency noise (LFN) spectroscopy. The findings reveal that the AATs exhibit anotable reduction in peak current with temperature instability and an abrupt decrease in drain current under applied DC bias. It is examined that these instabilities stem from defect-related carrier transport mechanisms at the n/p heterojunction, evidenced by the observation of 1/f 4 noise. Furthermore, a comprehensive comparative analysis is provided of 1/f 4 noise behavior with and without the insertion of an insulative layer of AAT. This provides themicroscopic origin of how the LFN generation mechanism changes the defect-related carrier conduction at the interface and mitigates the bias and temperature instabilities.

The impact of the intrinsic time-dependent fluctuations in the electrical resistance at the graphene–metal interface or the contact noise, on the performance of graphene field-effect transistors, can be as adverse as the contact resistance itself, but remains largely unexplored. Here we have investigated the contact noise in graphene field-effect transistors of varying device geometry and contact configuration, with carrier mobility ranging from 5,000 to 80,000 cm2 V−1 s−1. Our phenomenological model for contact noise because of current crowding in purely two-dimensional conductors confirms that the contacts dominate the measured resistance noise in all graphene field-effect transistors in the two-probe or invasive four-probe configurations, and surprisingly, also in nearly noninvasive four-probe (Hall bar) configuration in the high-mobility devices. The microscopic origin of contact noise is directly linked to the fluctuating electrostatic environment of the metal–channel interface, which could be generic to two-dimensional material-based electronic devices.

Flicker noise or 1/f noise refers to processes in which the power spectral density (PSD) is inversely proportional to frequency and is typically the dominant noise source in transistors at low frequency. We report our work on measurements of unprecedentedly low 1/f noise in graphene field effect transistors, which we attribute to large device area. Large area graphene grown by chemical vapor deposition (CVD) has potential for a variety of applications including biomolecular sensors, bolometric photodetectors and ion sensitive field effect transistors (ISFET) [1]. For all these applications, low-frequency 1/f noise is found to be the dominant factor that determines sensor resolution limits. Hence, the absolute value of 1/f noise PSD serves as a crucial performance metric for graphene sensor applications.Previous studies of 1/f noise in graphene devices has been performed using both CVD grown graphene and exfoliated graphene. Balandin et al [2] has studied 1/f noise in exfoliated graphene and how it varies with substrate and charge carrier concentration. Karnatak et al [3] has shown that contact resistance can play a dominant role in 1/f noise. To date, there has been no experimental study of 1/f as the graphene channel is scaled up to mm lengths.We report here our work on 1/f noise measurement of graphene field effect transistors with varying channel and contact geometries. In our experiments, CVD grown graphene on copper was transferred onto fused silica coated with 100 nm of parylene using a standard wet transfer process. Parylene was used as an interface between the graphene and fused silica, which has been shown by Fakih et al [1] to reduce both drift and hysteresis in graphene ISFETs. The transferred graphene was processed with two photolithography steps to fabricate devices with areas ranging from 12µm2 to 36mm2. Electrical contact to the graphene was made directly with Au. The 1/f noise was measured by first applying a dc bias using a low noise lithium-ion battery, and the generated voltage fluctuations were then measured using a low noise voltage pre-amplifier and a 24-bit digitizer.The voltage PSD of 1/f noise in graphene, will typically have the form of SV=Vo 2K/f. Where Vo is the dc voltage applied across the device, f is the frequency, and K is the unitless noise constant. K is experimentally approximated to be (1/N)∑nSVnfn/Vo 2, where SVn is the voltage PSD measured at n different frequencies fn. The work from Balandin et al [2] demonstrates typical K values of 10-8, and work from Karnatak et al [3] shows values as low as 10-9. Our devices have unprecedented low measured K values of 10-14, which we attribute to the large device area of 36 mm2. Our results experimentally demonstrate that the noise parameter can be decreased by orders of magnitude by working with large area devices. Our work suggests that for graphene based sensor applications, large device area is favourable for improved sensor resolution.

We propose an accurate and effective method, low-frequency noise (LFN) spectroscopy, to examine the resistive switching mechanism in ferroelectric tunnel junctions (FTJs) based on pure hafnium oxide (HfOx). Contrary to previous studies that primarily focused on the ferroelectric (FE) resistive switching (RS) in HfOx-based FTJs, the results of this study demonstrate that non-FE RS affected by the redistribution of oxygen vacancies also plays a significant role in determining the performance of FTJs. LFN spectroscopy is conducted in different conditions by changing the operating temperature and inducing DC cycling stress. The results reveal that the RS mechanism changes from FE to non-FE RS with increased program bias in all conditions. This change is facilitated by the rise in temperature and the number of DC cycling stress.

In this work we investigate the use of low-frequency noise (LFN) spectroscopy as a sensitive tool for investigation of semiconductor avalanche photodiode (APD) quality and reliability for high-speed communication applications. Samples from several manufacturing runs pre-screened through the standard production batch validation showed very low start of life low-frequency noise levels. The LFN test indicates the high quality of the devices with respect to noise characteristics. We also investigated the noise characteristics of production reject devices, some of which exhibit a 1/ f type noise peak at the guard ring punch-through voltages, and an increase in noise intensity at punch-through voltages after long term accelerated lifetesting (>1000 h at 200°C and 100 μA). Useful information on device quality can thus be obtained from the noise measurement results performed in the deep breakdown bias range (27-30 V), where a sharp peak of the Lorentzian type noise may be observed. This feature is believed to be due to intensive recombination processes at defects in interlayer regions. It is also shown that avalanche photodiodes containing some defects exhibit not only increased dark current and low-frequency noise level, but also increased multiplication excess noise factor which decreases with increasing input light intensity. This work shows that LFN spectroscopy is very useful for the localization of noise sources, and provides important information for further product quality improvement.

Concerns about indoor and outdoor air quality, industrial gas leaks, and medical diagnostics are driving the demand for high-performance gas sensors. Owing to their structural variety and large surface area, reducible metal oxides hold great promise for constructing a gas-sensing system. While many earlier reports have successfully obtained a sufficient response to various types of target gases, the selective detection of target gases remains challenging. In this work, a novel method, low-frequency noise (LFN) spectroscopy is presented, to achieve selective detection using a single FET-type gas sensor. The LFN of the sensor is accurately modeled by considering the charge fluctuation in both the sensing material and the FET channel. Exposure to different target gases produces distinct corner frequencies of the power spectral density that can be used to achieve selective detection. In addition, a 3D vertical-NAND flash array is used with the fast Fourier transform method via in-memory-computing, significantly improving the area and power efficiency rate. The proposed system provides a novel and efficient method capable of selectively detecting a target gas using in-memory-computed LFN spectroscopy and thus paving the way for the further development in gas sensing systems.

Protein-Lipid Interactions

The solvent of membrane proteins is the membrane lipids in which they are embedded. Therefore, the nature of the lipids that surround membrane proteins impacts their dynamics and interactions. Unfortunately, how membrane proteins dynamically interact is difficult to study, and little is experimentally known how membrane proteins interplay in a membrane at the molecular scale. Herein, high‐speed atomic force microscopy (HS‐AFM) is used to dynamically image a well‐controlled bottom‐up system consisting of two aquaporin‐fold membrane proteins, pentameric FocA and tetrameric GlpF, that interact in membranes composed of varying amounts of 1,2‐dioleoyl‐sn‐glycero‐3‐phosphocholine (DOPC) and E. coli lipids. It is found that the lipid environment significantly influences membrane protein mobility and interaction, where increased E. coli lipid content reduces protein movement, while DOPC‐rich environments promote mobility. Furthermore, the supramolecular structures of the membrane proteins and protomer interactions in clusters are also lipid modulated, where E. coli lipids favor specific protein–protein interactions, whereas greater interaction variability is found in DOPC. These findings highlight the role of lipids in regulating protein diffusion and interactions and suggest that lipid–protein interaction energetics play a significant role in controlling membrane protein interactions and supramolecular assembly.

Exploring membrane proteins dynamic in plant cells with fluorescence correlation spectroscopy

In this work a discussion on the estimated volume trap densities (NT) compared to surface traps densities (Neff) related to identified traps in the Si fin of Si/SiGe superlattice I/O n-channel FinFETs using low frequency noise spectroscopy is made. The investigated devices present a fin width of 10 nm, a fin height of 10 nm and four fins in parallel, leading to an equivalent channel width of 120 nm. The equivalent oxide thickness (EOT) is 5.6 nm. More details on the device fabrication and experimental setup may be found in [1,2]. The noise spectra and the estimated surface traps densities are provided in [3]. In this work, focus is only on the identified T4 trap, for which using the linear dependence that should exist between A0i and t0i related to the same trap, without any other assumption, a surface trap density of 2.8∙1012cm-2 is obtained [3]. It should be noted that from the 1/f flat-band noise level an interface trap density value of about 1.9∙1018 eV-1cm-3 is obtained at 300 K.However, as the traps in the Si film are related to a volume phenomenon, two methodologies to estimate the volume trap densities are employed: one using the relationship between the surface trap density and volume trap densitiy [4], where B is a coefficient estimated to be 1/3 [4,5]; and a second one from the temperature (T) evolution at fixed frequency of the Lorentzian plateau level associated to the same trap (from equation 34 in [4]). Using the first method leads to a volume trap density of T4 of about 1.7∙1019 cm-3.The second method consists to use the maximum of the measured Svg_Lor(f 0,T) dependence with temperature. Indeed, the Svg_Lor(f 0,T) of Lorentzians associated to the same trap are proportional with ti(T)/{1 + [2pf 0ti(T)]2}. For a given frequency f 0, if 2pf 0ti(T) ≫ 1, SVg_Lor(f 0,T) ∝ ti(T)]-1, and SVg_Lor(f 0,T) increases with increasing temperature because ti decreases. If 2pf 0ti(T) ≪ 1, then SVg_Lor(f 0,T) ∝ ti(T) and SVg_Lor(f 0,T) decreases with increasing temperature, as explained in detail in [4]. The evolution of the Svg_Lor(f 0,T)∙f 0 in a temperature range where T4 traps are active is illustrated in Figure 1 and presents a bell-shaped behavior, as expected. Using this method, volume trap densities of about 1.25∙1018 cm-3 for f 0 = 10 kHz and of about 1.1∙1018 cm-3 for f 0 = 14 kHz are obtained. It may be observed that the estimated volume trap densities of the T4 defect are about one decade lower than when using the first method. This overestimation by the first method is related to the fact that the theoretical B coefficient was determined for conventional planar devices with one gate [4,5]. Moreover, it may be noticed that the second method is dependent on the fixed f 0 selected.The paper will present a discussion with more details, considering all identified traps in [1] and considering additional new low frequency noise spectroscopy results.References :[1] Hellings, H. Mertens, A. Subirats, E. Simoen, T. Schram, L.-A. Ragnarsson et al., “Si/SiGe superlattice I/O finFETs in a vertically-stacked Gate-All-Around horizontal Nanowire Technology”, in Tech. Dig. Symp. on VLSI Technology, The IEEE New York, 2018, p.p. 85-86, DOI: 10.1109/VLSIT.2018.8510654.[2] Boudier, B. Cretu, E. Simoen, R. Carin, A. Veloso, N. Collaert, and A. Thean, “Low frequency noise assessment in n- and p-channel sub-10 nm triple-gate FinFETs: Part I: Theory and methodology,” Solid State Electron., vol. 128, pp. 102-108, 2017, DOI: 10.1016/j.sse.2016.10.012.[3] Boudier, B. Cretu, E. Simoen, G. Hellings, T. Schram, H. Mertens and D. Linten, “Low frequency noise analysis on Si/SiGe superlattice I/O n-channel FinFETs”, In Proceedings of EUROSOI-ULIS’2019.[4] Lukyanchikova, “Noise and Fluctuations Control in Electronic Device”, edited by A. Balandin, American Scientific, Riverside, CA, 2002, pp. 201-233.[5] Yau and C-T. Sah, “Theory and experiments of low-frequency generation-recombination noise in MOS transistors”, IEEE Trans. Electron Dev., 1969, vol.16, pp. 170-177.Figure 1 : SVg_Lor(f 0,T)·f 0 versus temperature for the T4 trap identified in [3]; on the secondary Oy axis the characteristic frequency f 0i of the Lorentzians is displayed in function of temperature. Figure 1