Evaluation of stability and robustness of poly-Si resistors with different dopant concentrations

The stability and robustness of lightly and highly doped poly-Si resistors were evaluated. These resistors exhibited distinct electrical resistance properties and temperature dependences, which can be explained through the grain and grain boundary conduction mechanisms. The resistance shift saturated under the low current stress condition, but continued to increase under the high current stress condition. A novel carrier trapping density model was proposed to explain this behavior. A generalized free energy model that considered stress temperature and stress current dependences was proposed to account for the stability lifetime of a poly-Si resistor based on the resistance shift criterion. Robustness evaluation with transmission line pulse test revealed that the breakdown current exhibited a pulse width dependence which was further explained by a thermal- conduction energy model.

Failure analysis for opening characteristic of high voltage and high-power silicon carbide module

According to the abnormal curve of opening characteristics in the process of double pulse test, the failure of 6.5kV/100A SiC module is judged, and it is preliminarily concluded that the gate of the module is damaged; The failure of the module is located by means of module anatomy, static characteristics test and chip screening test. It is concluded that the failure of the module is caused by the abnormality of two SiC MOSFET chips in the module; The damage of the chip was located by using stereomicroscope, OBIRCH, SEM and other equipment, and the root cause of MOSFET failure was studied by stripping anatomy. The preparation process of the gate and passivation strip on the MOSFET was poor, and the introduction of defects led to the breakdown of the gate unable to withstand high voltage.

Estimation and verification of fractional derivative-based permeability of coals considering mining-induced stresses

To overcome the inaccuracy of the traditional transient pulse test, a new fractional derivative-based permeability estimation formula based on the transient pulse test is proposed to describe the pressure difference decay of a coal body subjected to mining-induced stresses. The permeability of coal specimens under mining disturbance conditions is measured using the MTS815 rock mechanics test system. The experimental results show that the transient pulse test based on the fractional derivative model provides a much better estimation of the coal specimen’s permeability than the conventional exponential decay model. Analyzing the evolution of the coal’s permeability shows that the permeability tends to decrease in the pre-peak compaction stage, following which it gradually increases in the plastic phase, and then increases sharply in the post-peak phase. The significance of the fractional derivative order γ is discussed, and its analysis shows that the solid-liquid interaction inside the specimen becomes complicated when the stress within the coal specimen changes.

Machine Learning Provides Effective Leak Detection in Carbon Sequestration Projects

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201552, “Leak Detection in Carbon Sequestration Projects Using Machine Learning Methods: Cranfield Site, Mississippi, USA,” by Saurabh Sinha, SPE, University of Oklahoma and Los Alamos National Laboratory; Rafael Pires De Lima, Geological Survey of Brazil; and Youzuo Lin, Los Alamos National Laboratory, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, 5–7 October. The paper has not been peer reviewed. Saline aquifers and depleted hydrocarbon reservoirs with good seals located in tectonically stable zones make an excellent storage formation option for geological carbon sequestration.Ensuring that carbon dioxide (CO2) does not leak from these reservoirs is the key to any successful carbon capture and storage (CCS) project. In the complete paper, the authors demonstrate automated leakage detection in CCS projects using pressure data obtained from the Cranfield reservoir in Mississippi in the US. Results indicate that even simple deep-learning architectures such as multilayer feed-forward neural networks (MFNNs) can identify a leak using pressure data. Introduction Several methods that use different types of data currently are available to detect leaks. Although some of the methods are a direct indicator of CO2 presence, they cannot provide an early warning for the leaks, thus delaying remedial measures. An ideal process for the identification of leakages requires constant and repetitive comparisons of different data. Machine-learning (ML) techniques are ideally suited for this task. In this work, the authors demonstrate the use of ML techniques such as linear model, random forest, and MFNN on time-series signals obtained from a pressure-pulse test. The methodology uses the time-series data instead of 2D images or 3D voxels, thus providing a computational advantage. The authors write that an ML algorithm can distinguish between a pressure signal corresponding to a leak vs. the pressure signal corresponding to a baseline nonleak case. The trained models can then be used as an early-warning system to flag anomalous data to then be analyzed by a human interpreter. Background A pressure-pulse test uses at least two wells: an injection well and a monitoring well. The reservoir is then shocked by a series of predetermined cycles of injection and shut-ins (i.e., a pulse). The response then is recorded at the monitoring well with a pressure gauge that measures the target formation pressure. The test may be repeated with different pulses to understand the reservoir properties better. A harmonic pulse is preferred over a square wave because it allows for spectral decomposition of the pulse to analyze the reservoir response at different frequencies. Three wells are used in the study: F1, F2, and F3. Well F1 is the injector well, where alternative cycles of injection of CO2 and shut-in are carried out. Well F2 is the monitor well, which remains shut in for the duration of the test and where the pressure is monitored with the use of a pressure gauge. An artificial leak is simulated in the test by opening a surface valve at Well F3.

Mechanism Analysis of Dynamic On-State Resistance Degradation for a Commercial GaN HEMT Using Double Pulse Test

The dynamic on-resistance (RON) behavior of one commercial GaN HEMT device with p-GaN gate is investigated under hard-switching conditions. The non-monotonic performance of dynamic RON with off-state voltage ranging from 50 to 400 V is ascribed to the “leaky dielectric” model. The highest normalized RON value of 1.22 appears at 150 and 200 V. The gradual increase and following maximum of dynamic RON are found when the device is exposed to a stress voltage for an extended stress time under 100 and 200 V, which is due to a much longer trapping time compared to detrapping time related to deep acceptors and donors. No obvious RON degradation, thanks to the suppressed trapping effect, is observed at higher VDS. From the multi-pulse test, the dynamic RON is seen to be insensitive to the frequency. It is demonstrated that the leakage, especially under source and drain contact, is a key issue in the dynamic resistance degradation.