Reliability Investigations of MOSFETs using RF Small Signal Characterization

Modern technology needs and advancements have introduced various new concepts such as Internet-of-Things, electric automotive, and Artificial intelligence. This implies an increased activity in the electronics domain of analog and high frequency. Silicon devices have emerged as a cost-effective solution for such diverse applications. As these silicon devices are pushed towards higher performance, there is a continuous need to improve fabrication, power efficiency, variability, and reliability. Often, a direct trade-off of higher performance is observed in the reliability of semiconductor devices. The acceleration-based methodologies used for reliability assessment are the adequate time-saving solution for the lifetime's extrapolation but come with uncertainty in accuracy. Thus, the efforts to improve the accuracy of reliability characterization methodologies run in parallel. This study highlights two goals that can be achieved by incorporating high-frequency characterization into the reliability characteristics. The first one is assessing high-frequency performance throughout the device's lifetime to facilitate an accurate description of device/circuit functionality for high-frequency applications. Secondly, to explore the potential of high-frequency characterization as the means of scanning reliability effects within devices. S-parameters served as the high-frequency device's response and mapped onto a small-signal model to analyze different components of a fully depleted silicon-on-insulator MOSFET. The studied devices are subjected to two important DC stress patterns, i.e., Bias temperature instability stress and hot carrier stress. The hot carrier stress, which inherently suffers from the self-heating effect, resulted in the transistor's geometry-dependent magnitudes of hot carrier degradation. It is shown that the incorporation of the thermal resistance model is mandatory for the investigation of hot carrier degradation. The property of direct translation of small-signal parameter degradation to DC parameter degradation is used to develop a new S-parameter based bias temperature instability characterization methodology. The changes in gate-related small-signal capacitances after hot carrier stress reveals a distinct signature due to local change of flat-band voltage. The measured effects of gate-related small-signal capacitances post-stress are validated through transient physics-based simulations in Sentaurus TCAD.:Abstract Symbols Acronyms 1 Introduction 2 Fundamentals 2.1 MOSFETs Scaling Trends and Challenges 2.1.1 Silicon on Insulator Technology 2.1.2 FDSOI Technology 2.2 Reliability of Semiconductor Devices 2.3 RF Reliability 2.4 MOSFET Degradation Mechanisms 2.4.1 Hot Carrier Degradation 2.4.2 Bias Temperature Instability 2.5 Self-heating 3 RF Characterization of fully-depleted Silicon on Insulator devices 3.1 Scattering Parameters 3.2 S-parameters Measurement Flow 3.2.1 Calibration 3.2.2 De-embedding 3.3 Small-Signal Model 3.3.1 Model Parameters Extraction 3.3.2 Transistor Figures of Merit 3.4 Characterization Results 4 Self-heating assessment in Multi-finger Devices 4.1 Self-heating Characterization Methodology 4.1.1 Output Conductance Frequency dependence 4.1.2 Temperature dependence of Drain Current 4.2 Thermal Resistance Behavior 4.2.1 Thermal Resistance Scaling with number of fingers 4.2.2 Thermal Resistance Scaling with finger spacing 4.2.3 Thermal Resistance Scaling with GateWidth 4.2.4 Thermal Resistance Scaling with Gate length 4.3 Thermal Resistance Model 4.4 Design for Thermal Resistance Optimization 5 Bias Temperature Instability Investigation 5.1 Impact of Bias Temperature Instability stress on Device Metrics 5.1.1 Experimental Details 5.1.2 DC Parameters Drift 5.1.3 RF Small-Signal Parameters Drift 5.2 S-parameter based on-the-fly Bias Temperature Instability Characterization Method 5.2.1 Measurement Methodology 5.2.2 Results and Discussion 6 Investigation of Hot-carrier Degradation 6.1 Impact of Hot-carrier stress on Device performance 6.1.1 DC Metrics Degradation 6.1.2 Impact on small-signal Parameters 6.2 Implications of Self-heating on Hot-carrier Degradation in n-MOSFETs 6.2.1 Inclusion of Thermal resistance in Hot-carrier Degradation modeling 6.2.2 Convolution of Bias Temperature Instability component in Hot-carrier Degradation 6.2.3 Effect of Source and Drain Placement in Multi-finger Layout 6.3 Vth turn-around effect in p-MOSFET 7 Deconvolution of Hot-carrier Degradation and Bias Temperature Instability using Scattering parameters 7.1 Small-Signal Parameter Signatures for Hot-carrier Degradation and Bias Temperature Instability 7.2 TCAD Dynamic Simulation of Defects 7.2.1 Fixed Charges 7.2.2 Interface Traps near Gate 7.2.3 Interface Traps near Spacer Region 7.2.4 Combination of Traps 7.2.5 Drain Series Resistance effect 7.2.6 DVth Correction 7.3 Empirical Modeling based deconvolution of Hot-carrier Degradation 8 Conclusion and Recommendations 8.1 General Conclusions 8.2 Recommendations for Future Work A Directly measured S-parameters and extracted Y-parameters B Device Dimensions for Thermal Resistance Modeling C Frequency response of hot-carrier degradation (HCD) D Localization Effect of Interface Traps Bibliograph

Zinc Essentiality, Toxicity, and Its Bacterial Bioremediation: A Comprehensive Insight

Zinc (Zn) is one of the most abundantly found heavy metals in the Earth’s crust and is reported to be an essential trace metal required for the growth of living beings, with it being a cofactor of major proteins, and mediating the regulation of several immunomodulatory functions. However, its essentiality also runs parallel to its toxicity, which is induced through various anthropogenic sources, constant exposure to polluted sites, and other natural phenomena. The bioavailability of Zn is attributable to various vegetables, beef, and dairy products, which are a good source of Zn for safe consumption by humans. However, conditions of Zn toxicity can also occur through the overdosage of Zn supplements, which is increasing at an alarming rate attributing to lack of awareness. Though Zn toxicity in humans is a treatable and non-life-threatening condition, several symptoms cause distress to human activities and lifestyle, including fever, breathing difficulty, nausea, chest pain, and cough. In the environment, Zn is generally found in soil and water bodies, where it is introduced through the action of weathering, and release of industrial effluents, respectively. Excessive levels of Zn in these sources can alter soil and aquatic microbial diversity, and can thus affect the bioavailability and absorption of other metals as well. Several Gram-positive and -negative species, such as Bacillus sp., Staphylococcus sp., Streptococcus sp., and Escherichia coli, Pseudomonas sp., Klebsiella sp., and Enterobacter sp., respectively, have been reported to be promising agents of Zn bioremediation. This review intends to present an overview of Zn and its properties, uses, bioavailability, toxicity, as well as the major mechanisms involved in its bioremediation from polluted soil and wastewaters

Reliability Investigations of MOSFETs using RF Small Signal Characterization

Modern technology needs and advancements have introduced various new concepts such as Internet-of-Things, electric automotive, and Artificial intelligence. This implies an increased activity in the electronics domain of analog and high frequency. Silicon devices have emerged as a cost-effective solution for such diverse applications. As these silicon devices are pushed towards higher performance, there is a continuous need to improve fabrication, power efficiency, variability, and reliability. Often, a direct trade-off of higher performance is observed in the reliability of semiconductor devices. The acceleration-based methodologies used for reliability assessment are the adequate time-saving solution for the lifetime's extrapolation but come with uncertainty in accuracy. Thus, the efforts to improve the accuracy of reliability characterization methodologies run in parallel. This study highlights two goals that can be achieved by incorporating high-frequency characterization into the reliability characteristics. The first one is assessing high-frequency performance throughout the device's lifetime to facilitate an accurate description of device/circuit functionality for high-frequency applications. Secondly, to explore the potential of high-frequency characterization as the means of scanning reliability effects within devices. S-parameters served as the high-frequency device's response and mapped onto a small-signal model to analyze different components of a fully depleted silicon-on-insulator MOSFET. The studied devices are subjected to two important DC stress patterns, i.e., Bias temperature instability stress and hot carrier stress. The hot carrier stress, which inherently suffers from the self-heating effect, resulted in the transistor's geometry-dependent magnitudes of hot carrier degradation. It is shown that the incorporation of the thermal resistance model is mandatory for the investigation of hot carrier degradation. The property of direct translation of small-signal parameter degradation to DC parameter degradation is used to develop a new S-parameter based bias temperature instability characterization methodology. The changes in gate-related small-signal capacitances after hot carrier stress reveals a distinct signature due to local change of flat-band voltage. The measured effects of gate-related small-signal capacitances post-stress are validated through transient physics-based simulations in Sentaurus TCAD.:Abstract Symbols Acronyms 1 Introduction 2 Fundamentals 2.1 MOSFETs Scaling Trends and Challenges 2.1.1 Silicon on Insulator Technology 2.1.2 FDSOI Technology 2.2 Reliability of Semiconductor Devices 2.3 RF Reliability 2.4 MOSFET Degradation Mechanisms 2.4.1 Hot Carrier Degradation 2.4.2 Bias Temperature Instability 2.5 Self-heating 3 RF Characterization of fully-depleted Silicon on Insulator devices 3.1 Scattering Parameters 3.2 S-parameters Measurement Flow 3.2.1 Calibration 3.2.2 De-embedding 3.3 Small-Signal Model 3.3.1 Model Parameters Extraction 3.3.2 Transistor Figures of Merit 3.4 Characterization Results 4 Self-heating assessment in Multi-finger Devices 4.1 Self-heating Characterization Methodology 4.1.1 Output Conductance Frequency dependence 4.1.2 Temperature dependence of Drain Current 4.2 Thermal Resistance Behavior 4.2.1 Thermal Resistance Scaling with number of fingers 4.2.2 Thermal Resistance Scaling with finger spacing 4.2.3 Thermal Resistance Scaling with GateWidth 4.2.4 Thermal Resistance Scaling with Gate length 4.3 Thermal Resistance Model 4.4 Design for Thermal Resistance Optimization 5 Bias Temperature Instability Investigation 5.1 Impact of Bias Temperature Instability stress on Device Metrics 5.1.1 Experimental Details 5.1.2 DC Parameters Drift 5.1.3 RF Small-Signal Parameters Drift 5.2 S-parameter based on-the-fly Bias Temperature Instability Characterization Method 5.2.1 Measurement Methodology 5.2.2 Results and Discussion 6 Investigation of Hot-carrier Degradation 6.1 Impact of Hot-carrier stress on Device performance 6.1.1 DC Metrics Degradation 6.1.2 Impact on small-signal Parameters 6.2 Implications of Self-heating on Hot-carrier Degradation in n-MOSFETs 6.2.1 Inclusion of Thermal resistance in Hot-carrier Degradation modeling 6.2.2 Convolution of Bias Temperature Instability component in Hot-carrier Degradation 6.2.3 Effect of Source and Drain Placement in Multi-finger Layout 6.3 Vth turn-around effect in p-MOSFET 7 Deconvolution of Hot-carrier Degradation and Bias Temperature Instability using Scattering parameters 7.1 Small-Signal Parameter Signatures for Hot-carrier Degradation and Bias Temperature Instability 7.2 TCAD Dynamic Simulation of Defects 7.2.1 Fixed Charges 7.2.2 Interface Traps near Gate 7.2.3 Interface Traps near Spacer Region 7.2.4 Combination of Traps 7.2.5 Drain Series Resistance effect 7.2.6 DVth Correction 7.3 Empirical Modeling based deconvolution of Hot-carrier Degradation 8 Conclusion and Recommendations 8.1 General Conclusions 8.2 Recommendations for Future Work A Directly measured S-parameters and extracted Y-parameters B Device Dimensions for Thermal Resistance Modeling C Frequency response of hot-carrier degradation (HCD) D Localization Effect of Interface Traps Bibliograph

Reliability Investigations of MOSFETs using RF Small Signal Characterization

Modern technology needs and advancements have introduced various new concepts such as Internet-of-Things, electric automotive, and Artificial intelligence. This implies an increased activity in the electronics domain of analog and high frequency. Silicon devices have emerged as a cost-effective solution for such diverse applications. As these silicon devices are pushed towards higher performance, there is a continuous need to improve fabrication, power efficiency, variability, and reliability. Often, a direct trade-off of higher performance is observed in the reliability of semiconductor devices. The acceleration-based methodologies used for reliability assessment are the adequate time-saving solution for the lifetime's extrapolation but come with uncertainty in accuracy. Thus, the efforts to improve the accuracy of reliability characterization methodologies run in parallel. This study highlights two goals that can be achieved by incorporating high-frequency characterization into the reliability characteristics. The first one is assessing high-frequency performance throughout the device's lifetime to facilitate an accurate description of device/circuit functionality for high-frequency applications. Secondly, to explore the potential of high-frequency characterization as the means of scanning reliability effects within devices. S-parameters served as the high-frequency device's response and mapped onto a small-signal model to analyze different components of a fully depleted silicon-on-insulator MOSFET. The studied devices are subjected to two important DC stress patterns, i.e., Bias temperature instability stress and hot carrier stress. The hot carrier stress, which inherently suffers from the self-heating effect, resulted in the transistor's geometry-dependent magnitudes of hot carrier degradation. It is shown that the incorporation of the thermal resistance model is mandatory for the investigation of hot carrier degradation. The property of direct translation of small-signal parameter degradation to DC parameter degradation is used to develop a new S-parameter based bias temperature instability characterization methodology. The changes in gate-related small-signal capacitances after hot carrier stress reveals a distinct signature due to local change of flat-band voltage. The measured effects of gate-related small-signal capacitances post-stress are validated through transient physics-based simulations in Sentaurus TCAD.:Abstract Symbols Acronyms 1 Introduction 2 Fundamentals 2.1 MOSFETs Scaling Trends and Challenges 2.1.1 Silicon on Insulator Technology 2.1.2 FDSOI Technology 2.2 Reliability of Semiconductor Devices 2.3 RF Reliability 2.4 MOSFET Degradation Mechanisms 2.4.1 Hot Carrier Degradation 2.4.2 Bias Temperature Instability 2.5 Self-heating 3 RF Characterization of fully-depleted Silicon on Insulator devices 3.1 Scattering Parameters 3.2 S-parameters Measurement Flow 3.2.1 Calibration 3.2.2 De-embedding 3.3 Small-Signal Model 3.3.1 Model Parameters Extraction 3.3.2 Transistor Figures of Merit 3.4 Characterization Results 4 Self-heating assessment in Multi-finger Devices 4.1 Self-heating Characterization Methodology 4.1.1 Output Conductance Frequency dependence 4.1.2 Temperature dependence of Drain Current 4.2 Thermal Resistance Behavior 4.2.1 Thermal Resistance Scaling with number of fingers 4.2.2 Thermal Resistance Scaling with finger spacing 4.2.3 Thermal Resistance Scaling with GateWidth 4.2.4 Thermal Resistance Scaling with Gate length 4.3 Thermal Resistance Model 4.4 Design for Thermal Resistance Optimization 5 Bias Temperature Instability Investigation 5.1 Impact of Bias Temperature Instability stress on Device Metrics 5.1.1 Experimental Details 5.1.2 DC Parameters Drift 5.1.3 RF Small-Signal Parameters Drift 5.2 S-parameter based on-the-fly Bias Temperature Instability Characterization Method 5.2.1 Measurement Methodology 5.2.2 Results and Discussion 6 Investigation of Hot-carrier Degradation 6.1 Impact of Hot-carrier stress on Device performance 6.1.1 DC Metrics Degradation 6.1.2 Impact on small-signal Parameters 6.2 Implications of Self-heating on Hot-carrier Degradation in n-MOSFETs 6.2.1 Inclusion of Thermal resistance in Hot-carrier Degradation modeling 6.2.2 Convolution of Bias Temperature Instability component in Hot-carrier Degradation 6.2.3 Effect of Source and Drain Placement in Multi-finger Layout 6.3 Vth turn-around effect in p-MOSFET 7 Deconvolution of Hot-carrier Degradation and Bias Temperature Instability using Scattering parameters 7.1 Small-Signal Parameter Signatures for Hot-carrier Degradation and Bias Temperature Instability 7.2 TCAD Dynamic Simulation of Defects 7.2.1 Fixed Charges 7.2.2 Interface Traps near Gate 7.2.3 Interface Traps near Spacer Region 7.2.4 Combination of Traps 7.2.5 Drain Series Resistance effect 7.2.6 DVth Correction 7.3 Empirical Modeling based deconvolution of Hot-carrier Degradation 8 Conclusion and Recommendations 8.1 General Conclusions 8.2 Recommendations for Future Work A Directly measured S-parameters and extracted Y-parameters B Device Dimensions for Thermal Resistance Modeling C Frequency response of hot-carrier degradation (HCD) D Localization Effect of Interface Traps Bibliograph

Repellency, Toxicity, Gene Expression Profiling and In Silico Studies to Explore Insecticidal Potential of <i>Melaleuca alternifolia</i> Essential Oil against <i>Myzus persicae</i>

In the current study, deterrent assay, contact bioassay, lethal concentration (LC) analysis and gene expression analysis were performed to reveal the repellent or insecticidal potential of M. alternifolia oil against M. persicae. M. alternifolia oil demonstrated an excellent deterrence index (0.8) at 2 g/L after 48 h. The oil demonstrated a pronounced contact mortality rate (72%) at a dose of 4 g/L after 24 h. Probit analysis was performed to estimate LC-values of M. alternifolia oil (40%) against M. persicae (LC30 = 0.115 g/L and LC50 = 0.37 g/L respectively) after 24 h. Furthermore, to probe changes in gene expression due to M. alternifolia oil contact in M. persicae, the expression of HSP 60, FPPS I, OSD, TOL and ANT genes were examined at doses of LC30 and LC50. Four out of the five selected genes&#8212;OSD, ANT, HSP 60 and FPPS I&#8212;showed upregulation at LC50, whereas, TOL gene showed maximum upregulation expression at LC30. Finally, the major components of M. alternifolia oil (terpinen-4-ol) were docked and MD simulated into the related proteins of the selected genes to explore ligand&#8315;protein modes of interactions and changes in gene expression. The results show that M. alternifolia oil has remarkable insecticidal and deterrent effects and also has the ability to affect the reproduction and development in M. persicae by binding to proteins

Off-state Impact on FDSOI Ring Oscillator Degradation under High Voltage Stress

The degradation predicted by classical DC reliability methods, such as bias temperature instability (BTI) and hot carrier injection (HCI), might not translate sufficiently to the AC conditions, which are relevant on the circuit level. The direct analysis of circuit level reliability is therefore an essential task for hardware qualification in the near future. Ring oscillators (RO) offer a good model system, where both BTI and HCI contribute to the degradation. In this work, it is qualitatively shown that the additional off-state stress plays a crucial role at very high stress voltages, beyond upper usage boundaries. To yield an accurate RO lifetime prediction a frequency measurement setup with high resolution is used, which can resolve small changes in frequency during stress near operation conditions. An ACDC conversion model is developed predicting the resulting frequency change based on DC input data. From the extrapolation to 10 years of circuit lifetime the model predicts a very low frequency degradation below 0.2% under nominal operation conditions, where the off-state has a minor influence

Impact of BTI Stress on RF Small Signal Parameters of FDSOI MOSFETs

The growing interest in high speed and RF technologies assert for the importance of reliability characterization beyond the conventional DC methodology. In this work, the influence of bias temperature instability (BTI) stress on RF small signal parameters is shown. The correlation between degradation of DC and RF parameters is established which enables the empirical modelling of stress induced changes. Furthermore, S-Parameters characterization is demonstrated as the tool to qualitatively distinguish between HCI and BTI degradation mechanisms with the help of extracted small signal gate capacitances

Molecular modeling of pyrrolo-pyrimidine based analogs as potential FGFR1 inhibitors: a scientific approach for therapeutic drugs

Fibroblast growth factor receptors 1 (FGFR1) is an emerging target for the development of anticancer drugs. Uncontrolled expression of FGFR1 is strongly associated with a number of different types of cancers. Apart from a few FGFR inhibitors, the FGFR family members have not been thoroughly studied to produce clinically effective anticancer drugs. The application of proper computational techniques may aid in understanding the mechanism of protein–ligand complex formation, which may provide a better notion for developing potent FGFR1 inhibitors. In this study, a variety of computational techniques, including 3D-QSAR, flexible docking and MD simulation followed by MMGB/PBSA, H-bonds and distance analysis, have been performed to systematically explore the binding mechanism of pyrrolo-pyrimidine derivatives against FGFR1. The 3D-QSAR model was generated to deduce the structural determinants of FGFR1 inhibition. The high q2 and r2 values for the CoMFA and CoMSIA models indicated that the created 3D-QSAR models could reliably predict the bioactivities of FGFR1 inhibitors. The computed binding free energies (MMGB/PBSA) for the selected compounds were consistent with the ranking of their experimental binding affinities against FGFR1. Furthermore, per-residue energy decomposition analysis revealed that the residues Lys514 in catalytic region, Asn568, Glu571 in solvent accessible portion and Asp641 in DFG motif exhibited a strong tendency to mediate ligand–protein interactions through the hydrogen bonding and Van Der Waals interactions. These findings may benefit researchers in gaining better knowledge of FGFR1 inhibition and may serve as a guideline for the development of novel and highly effective FGFR1 inhibitors. Communicated by Ramaswamy H. Sarma</p