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Electron. Mater., Volume 4, Issue 2 (June 2023) – 4 articles

Cover Story (view full-size image): The behavior of materials often escapes models. While sensors calibrate models, algorithms treat physical outputs as data to learn finding one. As the behavior of organic semiconductors’ is often complex, the physical approach entangles their use in sensors to confirm what can be modeled and hinders the benefits of their full potential when treated as a data generator. The PEDOT:PSS conducting polymer is not selective; however, multivariate data analysis succeeds in recognizing its ion fingerprints on an electrochemical transistor. One material on one device suffices to cluster cations in water, allowing for the use of microsensors’ full performances where a multi-material approach is unfeasible, and understanding them by using up-to-date data-driven algorithms to unravel that properties that are not yet theorized. View this paper
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15 pages, 3013 KiB  
Article
Exploring the Impact of Fe-Implantation on the Electrical Characteristics of Al/p-Si Schottky Barrier Diodes
by Joseph Oluwadamilola Bodunrin, Duke Ateyh Oeba and Sabata Jonas Moloi
Electron. Mater. 2023, 4(2), 95-109; https://doi.org/10.3390/electronicmat4020008 - 16 Jun 2023
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Abstract
The effects of Fe-implantation on the electrical characteristics of Au/p-Si Schottky barrier diodes (SBDs) were studied using current–voltage (IV) and capacitance–voltage (CV) techniques. The Rutherford Backscattering Spectrometry (RBS) and Energy Dispersive [...] Read more.
The effects of Fe-implantation on the electrical characteristics of Au/p-Si Schottky barrier diodes (SBDs) were studied using current–voltage (IV) and capacitance–voltage (CV) techniques. The Rutherford Backscattering Spectrometry (RBS) and Energy Dispersive Spectroscopy (EDS) results showed that Fe ions are well implanted and present in the Fe-implanted Si material. The acquired results from IV and CV analysis showed that the diodes were well fabricated, and Fe-implantation changed the normal diode’s IV behaviour from typical exponential to ohmic. The ohmic behaviour was described in terms of the defect levels induced by Fe in the middle of the band gap of Si. The conduction mechanism for both forward and reverse currents was presented, and the effect of Fe-implantation on the conduction mechanisms was investigated. The CV results show that Fe generates a high density of minority carriers in p-Si, which agreed with the increase in reverse current observed in the IV results. The diode parameters in terms of saturation current, ideality factor, Schottky barrier height, doping density, and space charge region (SCR) width were used to investigate the effect of Fe in p-Si based diode. Owing to the observed changes, which were analogous to those induced by dopants that improve the radiation hardness of silicon, it was safe to say that Fe can also assist in the quest to improve the radiation hardness of silicon using the defect-engineering method. Full article
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15 pages, 3349 KiB  
Article
A Neural Network to Decipher Organic Electrochemical Transistors’ Multivariate Responses for Cation Recognition
by Sébastien Pecqueur, Dominique Vuillaume, Željko Crljen, Ivor Lončarić and Vinko Zlatić
Electron. Mater. 2023, 4(2), 80-94; https://doi.org/10.3390/electronicmat4020007 - 18 May 2023
Cited by 2 | Viewed by 1528
Abstract
Extracting relevant data from real-world experiments is often challenging with intrinsic materials and device property dispersion, such as in organic electronics. However, multivariate data analysis can often be a mean to circumvent this and to extract more information when larger datasets are used [...] Read more.
Extracting relevant data from real-world experiments is often challenging with intrinsic materials and device property dispersion, such as in organic electronics. However, multivariate data analysis can often be a mean to circumvent this and to extract more information when larger datasets are used with learning algorithms instead of physical models. Here, we report on identifying relevant information descriptors for organic electrochemical transistors (OECTs) to classify aqueous electrolytes by ionic composition. Applying periodical gate pulses at different voltage magnitudes, we extracted a reduced number of nonredundant descriptors from the rich drain-current dynamics, which provide enough information to cluster electrochemical data by principal component analysis between Ca2+-, K+-, and Na+-rich electrolytes. With six current values obtained at the appropriate time domain of the device charge/discharge transient, one can identify the cationic identity of a locally probed transient current with only a single micrometric device. Applied to OECT-based neural sensors, this analysis demonstrates the capability for a single nonselective device to retrieve the rich ionic identity of neural activity at the scale of each neuron individually when learning algorithms are applied to the device physics. Full article
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18 pages, 3838 KiB  
Article
Modelling of Low-Voltage Varistors’ Responses under Slow-Front Overvoltages
by Lutendo Muremi, Pitshou N. Bokoro and Wesley Doorsamy
Electron. Mater. 2023, 4(2), 62-79; https://doi.org/10.3390/electronicmat4020006 - 09 May 2023
Viewed by 1427
Abstract
In this study, commercially low-voltage MOVs are exposed to switching surges to analyse and model the relationship between the number of surges and the MOV grain barrier height response. Repeated slow-front overvoltage transients are used to degrade the protective qualities of metal oxide [...] Read more.
In this study, commercially low-voltage MOVs are exposed to switching surges to analyse and model the relationship between the number of surges and the MOV grain barrier height response. Repeated slow-front overvoltage transients are used to degrade the protective qualities of metal oxide surge arrester devices, affecting their reliability and stability. A total of 360 MOVs with similar specifications from three different manufacturers are degraded under switching surges at a constant temperature of 60 °C. The reference voltage and C-V characteristics of MOVs are measured before and after the degradation process to analyse the MOVs’ conditions. Grain barrier heights are determined from the C-V characteristics curve. An F-statistical analysis is then applied to analyse the effects of number of surges on the grain barrier height. The T-test is used to assess the statistical difference between the tested groups. Linear regression analysis is then applied to model the relationship between the number of surges and MOV grain barrier height. The results obtained show that the number of surges has a significant impact on grain barrier height. MOV grain barrier height is found to decrease as the number of surges applied increases. Regression models obtained for the tested MOV groups across all three manufacturers agree and indicate that the reduction in grain barrier height results from an increased number of surges. Regression coefficients of a developed model indicate that for one surge applied, the MOV grain barrier height decreases by 0.024, 0.055, and 0.033 eV/cm for manufacturers X, Y, and Z, respectively. Therefore, there is a linear relationship between grain barrier height and the number of applied switching surges. Full article
(This article belongs to the Special Issue Metal Oxide Semiconductors for Electronic Applications)
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13 pages, 7280 KiB  
Perspective
On-Surface Synthesis and Applications of 2D Covalent Organic Framework Nanosheets
by Jinwei Fan, Zhuoqun Wang, Haoge Cheng, Dingguan Wang and Andrew Thye Shen Wee
Electron. Mater. 2023, 4(2), 49-61; https://doi.org/10.3390/electronicmat4020005 - 12 Apr 2023
Cited by 1 | Viewed by 2244
Abstract
Covalent organic framework nanosheets (COF nanosheets) are two-dimensional crystalline porous polymers with in-plane covalent bonds and out-of-plane Van der Waals forces. Owing to the customizable structure, chemical modification, and ultra-high porosity, COF nanosheets show many fascinating properties unique to traditional two-dimensional materials, and [...] Read more.
Covalent organic framework nanosheets (COF nanosheets) are two-dimensional crystalline porous polymers with in-plane covalent bonds and out-of-plane Van der Waals forces. Owing to the customizable structure, chemical modification, and ultra-high porosity, COF nanosheets show many fascinating properties unique to traditional two-dimensional materials, and have shown potential applications in gas separation, sensors, electronic, and optoelectronic devices. This minireview aims to illustrate recent progress on two-dimensional covalent organic framework nanosheets, from two aspects of on-surface synthesis and potential applications. We first review the synthesis of COF nanosheets at the gas–solid interface. On-surface synthesis under ultrahigh vacuum and on-surface synthesis under vapor are highlighted. In addition, we also review the liquid–solid interface synthesis of COF nanosheets at various substrates, i.e., both crystalline and amorphous substrates. Beyond the synthesis, we highlight state-of-the-art applications of the COF nanosheets, particularly in charge transport, chemical sensors, and gas separation. Full article
(This article belongs to the Special Issue Feature Papers of Electronic Materials II)
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