Pub Date : 2023-06-21DOI: 10.3389/femat.2023.1174159
Harshit Sharma, Ritu Srivastava
In recent years, perovskite material-based photovoltaic devices have attracted great attention of researchers because of an expeditious improvement in their efficiency from 3.8% to over 25%. The electron transport layer (ETL), which functions for the extraction and transportation of photogenerated electrons from active perovskite material to the electrodes, is a vital part of these perovskite solar cells (PSCs). The optoelectronic properties of these electron transport layer materials also have an impact on the performance of these perovskite solar cells, and for commercialized flexible perovskite solar cells, low-temperature and solution-processable electron transport layers having high stability and suitable optoelectronic properties are needed. In this regard, the solution-processable films of different metal oxides have been largely investigated by many research groups. So, this review summarizes the optoelectronic properties of the different metal oxide-based electron transport layers and the development in the performance of the perovskite solar cells, which have solution-processable metal oxides as electron transport layers.
{"title":"Solution-processed pristine metal oxides as electron-transporting materials for perovskite solar cells","authors":"Harshit Sharma, Ritu Srivastava","doi":"10.3389/femat.2023.1174159","DOIUrl":"https://doi.org/10.3389/femat.2023.1174159","url":null,"abstract":"In recent years, perovskite material-based photovoltaic devices have attracted great attention of researchers because of an expeditious improvement in their efficiency from 3.8% to over 25%. The electron transport layer (ETL), which functions for the extraction and transportation of photogenerated electrons from active perovskite material to the electrodes, is a vital part of these perovskite solar cells (PSCs). The optoelectronic properties of these electron transport layer materials also have an impact on the performance of these perovskite solar cells, and for commercialized flexible perovskite solar cells, low-temperature and solution-processable electron transport layers having high stability and suitable optoelectronic properties are needed. In this regard, the solution-processable films of different metal oxides have been largely investigated by many research groups. So, this review summarizes the optoelectronic properties of the different metal oxide-based electron transport layers and the development in the performance of the perovskite solar cells, which have solution-processable metal oxides as electron transport layers.","PeriodicalId":119676,"journal":{"name":"Frontiers in Electronic Materials","volume":"405 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135045918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-26DOI: 10.3389/femat.2023.1061269
C. Bengel, Kaihua Zhang, J. Mohr, Tobias Ziegler, S. Wiefels, R. Waser, D. Wouters, S. Menzel
The proliferation of machine learning algorithms in everyday applications such as image recognition or language translation has increased the pressure to adapt underlying computing architectures towards these algorithms. Application specific integrated circuits (ASICs) such as the Tensor Processing Units by Google, Hanguang by Alibaba or Inferentia by Amazon Web Services were designed specifically for machine learning algorithms and have been able to outperform CPU based solutions by great margins during training and inference. As newer generations of chips allow handling of and computation on more and more data, the size of neural networks has dramatically increased, while the challenges they are trying to solve have become more complex. Neuromorphic computing tries to take inspiration from biological information processing systems, aiming to further improve the efficiency with which these networks can be trained or the inference can be performed. Enhancing neuromorphic computing architectures with memristive devices as non-volatile storage elements could potentially allow for even higher energy efficiencies. Their ability to mimic synaptic plasticity dynamics brings neuromorphic architectures closer to the biological role models. So far, memristive devices are mainly investigated for the emulation of the weights of neural networks during training and inference as their non-volatility would enable both processes in the same location without data transfer. In this paper, we explore realisations of different synapses build from memristive ReRAM devices, based on the Valence Change Mechanism. These synapses are the 1R synapse, the NR synapse and the 1T1R synapse. For the 1R synapse, we propose three dynamical regimes and explore their performance through different synapse criteria. For the NR synapse, we discuss how the same dynamical regimes can be addressed in a more reliable way. We also show experimental results measured on ZrOx devices to support our simulation based claims. For the 1T1R synapse, we explore the trade offs between the connection direction of the ReRAM device and the transistor. For all three synapse concepts we discuss the impact of device-to-device and cycle-to-cycle variability. Additionally, the impact of the stimulation mode on the observed behavior is discussed.
机器学习算法在日常应用(如图像识别或语言翻译)中的激增，增加了使底层计算架构适应这些算法的压力。专用集成电路(asic)，如谷歌的张量处理单元(Tensor Processing Units)、阿里巴巴的汉光(hanang)或亚马逊网络服务(Amazon Web Services)的interentia，都是专门为机器学习算法设计的，在训练和推理过程中，它们的性能大大超过了基于CPU的解决方案。随着新一代芯片允许处理和计算越来越多的数据，神经网络的规模急剧增加，而它们试图解决的挑战也变得更加复杂。神经形态计算试图从生物信息处理系统中获得灵感，旨在进一步提高这些网络的训练效率或执行推理的效率。使用忆阻器件作为非易失性存储元件来增强神经形态计算架构可能会带来更高的能源效率。它们模仿突触可塑性动态的能力使神经形态结构更接近生物学的角色模型。到目前为止，记忆装置主要用于神经网络在训练和推理过程中的权重仿真，因为记忆装置的非易失性可以使两个过程在同一位置进行，而无需传输数据。在本文中，我们探索了基于价变机制的记忆性ReRAM器件构建不同突触的实现。这些突触是1R突触，NR突触和1T1R突触。对于1R突触，我们提出了三种动态机制，并通过不同的突触标准探讨了它们的性能。对于NR突触，我们讨论了如何以更可靠的方式处理相同的动态机制。我们还展示了在ZrOx设备上测量的实验结果，以支持我们基于仿真的主张。对于1T1R突触，我们探索了ReRAM器件和晶体管连接方向之间的权衡。对于所有三个突触概念，我们将讨论设备对设备和周期对周期可变性的影响。此外，还讨论了刺激模式对观察行为的影响。
{"title":"Tailor-made synaptic dynamics based on memristive devices","authors":"C. Bengel, Kaihua Zhang, J. Mohr, Tobias Ziegler, S. Wiefels, R. Waser, D. Wouters, S. Menzel","doi":"10.3389/femat.2023.1061269","DOIUrl":"https://doi.org/10.3389/femat.2023.1061269","url":null,"abstract":"The proliferation of machine learning algorithms in everyday applications such as image recognition or language translation has increased the pressure to adapt underlying computing architectures towards these algorithms. Application specific integrated circuits (ASICs) such as the Tensor Processing Units by Google, Hanguang by Alibaba or Inferentia by Amazon Web Services were designed specifically for machine learning algorithms and have been able to outperform CPU based solutions by great margins during training and inference. As newer generations of chips allow handling of and computation on more and more data, the size of neural networks has dramatically increased, while the challenges they are trying to solve have become more complex. Neuromorphic computing tries to take inspiration from biological information processing systems, aiming to further improve the efficiency with which these networks can be trained or the inference can be performed. Enhancing neuromorphic computing architectures with memristive devices as non-volatile storage elements could potentially allow for even higher energy efficiencies. Their ability to mimic synaptic plasticity dynamics brings neuromorphic architectures closer to the biological role models. So far, memristive devices are mainly investigated for the emulation of the weights of neural networks during training and inference as their non-volatility would enable both processes in the same location without data transfer. In this paper, we explore realisations of different synapses build from memristive ReRAM devices, based on the Valence Change Mechanism. These synapses are the 1R synapse, the NR synapse and the 1T1R synapse. For the 1R synapse, we propose three dynamical regimes and explore their performance through different synapse criteria. For the NR synapse, we discuss how the same dynamical regimes can be addressed in a more reliable way. We also show experimental results measured on ZrOx devices to support our simulation based claims. For the 1T1R synapse, we explore the trade offs between the connection direction of the ReRAM device and the transistor. For all three synapse concepts we discuss the impact of device-to-device and cycle-to-cycle variability. Additionally, the impact of the stimulation mode on the observed behavior is discussed.","PeriodicalId":119676,"journal":{"name":"Frontiers in Electronic Materials","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130690387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-09DOI: 10.3389/femat.2022.1120381
J. Lynn, V. Madhavan, L. Jiao
NIST Center for Neutron Research, National Institute of Standards and Technology (NIST), Gaithersburg, MD, United States, Department of Physics, University of Illinois, Urbana-Champaign, Champaign, IL, United States, Center for Correlated Matter, School of Physics, Zhejiang University, Hangzhou, China, National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, United States
{"title":"Editorial: New heavy fermion superconductors","authors":"J. Lynn, V. Madhavan, L. Jiao","doi":"10.3389/femat.2022.1120381","DOIUrl":"https://doi.org/10.3389/femat.2022.1120381","url":null,"abstract":"NIST Center for Neutron Research, National Institute of Standards and Technology (NIST), Gaithersburg, MD, United States, Department of Physics, University of Illinois, Urbana-Champaign, Champaign, IL, United States, Center for Correlated Matter, School of Physics, Zhejiang University, Hangzhou, China, National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, United States","PeriodicalId":119676,"journal":{"name":"Frontiers in Electronic Materials","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124682471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-04DOI: 10.3389/femat.2022.1107802
Jianqing Wu, Jiajia Tao, Chuansheng Zhang, Haoxing Zhang, Lei Zhang, Dong Chen, Xiaodong Wang
Terahertz (THz) Si-based blocked-impurity-band (BIB) detector is becoming the overwhelming choice for applications in space-based instruments, airborne, and imaging systems. A high-performance linear scan imaging system based on the THz Si-based BIB detector is designed. Through the optimized design of cryogenic Dewar and a suitable optical system, the imaging system reduces background stray radiation, and then improves the THz imaging performance of the detector. At the temperature of 4.2 K and the bias of 2.6V, the blackbody peak responsivity of the Si-based BIB detector is 23.77A/W, while the dark current is 4.72 × 10 − 11 A and the corresponding responsivity non-uniformity is less than 6.8%. Moreover, the experiment results show that the noise equivalent temperature difference (NETD) of the whole system reaches 10mK, and the spatial resolution reaches 50 µm. This work is beneficial to the larger scale array integrated BIB imaging system.
{"title":"The high-performance linear scan imaging system of terahertz Si-based blocked-impurity-band detector","authors":"Jianqing Wu, Jiajia Tao, Chuansheng Zhang, Haoxing Zhang, Lei Zhang, Dong Chen, Xiaodong Wang","doi":"10.3389/femat.2022.1107802","DOIUrl":"https://doi.org/10.3389/femat.2022.1107802","url":null,"abstract":"Terahertz (THz) Si-based blocked-impurity-band (BIB) detector is becoming the overwhelming choice for applications in space-based instruments, airborne, and imaging systems. A high-performance linear scan imaging system based on the THz Si-based BIB detector is designed. Through the optimized design of cryogenic Dewar and a suitable optical system, the imaging system reduces background stray radiation, and then improves the THz imaging performance of the detector. At the temperature of 4.2 K and the bias of 2.6V, the blackbody peak responsivity of the Si-based BIB detector is 23.77A/W, while the dark current is 4.72 × 10 − 11 A and the corresponding responsivity non-uniformity is less than 6.8%. Moreover, the experiment results show that the noise equivalent temperature difference (NETD) of the whole system reaches 10mK, and the spatial resolution reaches 50 µm. This work is beneficial to the larger scale array integrated BIB imaging system.","PeriodicalId":119676,"journal":{"name":"Frontiers in Electronic Materials","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133190725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-19DOI: 10.3389/femat.2022.1010613
Minhui Zou, Nan Du, Shahar Kvatinsky
Neural network (NN) algorithms have become the dominant tool in visual object recognition, natural language processing, and robotics. To enhance the computational efficiency of these algorithms, in comparison to the traditional von Neuman computing architectures, researchers have been focusing on memristor computing systems. A major drawback when using memristor computing systems today is that, in the artificial intelligence (AI) era, well-trained NN models are intellectual property and, when loaded in the memristor computing systems, face theft threats, especially when running in edge devices. An adversary may steal the well-trained NN models through advanced attacks such as learning attacks and side-channel analysis. In this paper, we review different security techniques for protecting memristor computing systems. Two threat models are described based on their assumptions regarding the adversary’s capabilities: a black-box (BB) model and a white-box (WB) model. We categorize the existing security techniques into five classes in the context of these threat models: thwarting learning attacks (BB), thwarting side-channel attacks (BB), NN model encryption (WB), NN weight transformation (WB), and fingerprint embedding (WB). We also present a cross-comparison of the limitations of the security techniques. This paper could serve as an aid when designing secure memristor computing systems.
{"title":"Review of security techniques for memristor computing systems","authors":"Minhui Zou, Nan Du, Shahar Kvatinsky","doi":"10.3389/femat.2022.1010613","DOIUrl":"https://doi.org/10.3389/femat.2022.1010613","url":null,"abstract":"Neural network (NN) algorithms have become the dominant tool in visual object recognition, natural language processing, and robotics. To enhance the computational efficiency of these algorithms, in comparison to the traditional von Neuman computing architectures, researchers have been focusing on memristor computing systems. A major drawback when using memristor computing systems today is that, in the artificial intelligence (AI) era, well-trained NN models are intellectual property and, when loaded in the memristor computing systems, face theft threats, especially when running in edge devices. An adversary may steal the well-trained NN models through advanced attacks such as learning attacks and side-channel analysis. In this paper, we review different security techniques for protecting memristor computing systems. Two threat models are described based on their assumptions regarding the adversary’s capabilities: a black-box (BB) model and a white-box (WB) model. We categorize the existing security techniques into five classes in the context of these threat models: thwarting learning attacks (BB), thwarting side-channel attacks (BB), NN model encryption (WB), NN weight transformation (WB), and fingerprint embedding (WB). We also present a cross-comparison of the limitations of the security techniques. This paper could serve as an aid when designing secure memristor computing systems.","PeriodicalId":119676,"journal":{"name":"Frontiers in Electronic Materials","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125741575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-13DOI: 10.3389/femat.2022.1044620
K. Shrestha, L. Deng, B. Lv, C. Chu
This work presents the evolution of magnetic properties of EuxCa1−x Fe2As2 (0 ≤ x ≤ 1, ECFA) samples. Unlike the resistivity data, that for magnetic susceptibility χ (T) does not show any clear evidence of the spin density wave (SDW) transition. When the Curie-Weiss contribution is subtracted, a weak anomaly appears at a temperature close to the SDW transition temperature (T SDW) determined from the resistivity data. To understand the magnetic orders arising from Fe-moments and Eu2+ spins order, we have studied the doping dependence of T SDW and Eu2+ antiferromagnetic order T N . It is found that T SDW increases almost linearly with increasing x and remains nearly unchanged above x ∼ 0.4, whereas T N first appears at x ∼ 0.4 and varies almost linearly with further increasing x. These observations suggest that magnetic orders due to two sublattices are coupled to each other. The results discussed here are helpful for understanding the magnetic properties of ECFA and other iron-based superconductors.
{"title":"Evolution of magnetic properties in iron-based superconductor Eu-doped CaFe2As2","authors":"K. Shrestha, L. Deng, B. Lv, C. Chu","doi":"10.3389/femat.2022.1044620","DOIUrl":"https://doi.org/10.3389/femat.2022.1044620","url":null,"abstract":"This work presents the evolution of magnetic properties of EuxCa1−x Fe2As2 (0 ≤ x ≤ 1, ECFA) samples. Unlike the resistivity data, that for magnetic susceptibility χ (T) does not show any clear evidence of the spin density wave (SDW) transition. When the Curie-Weiss contribution is subtracted, a weak anomaly appears at a temperature close to the SDW transition temperature (T SDW) determined from the resistivity data. To understand the magnetic orders arising from Fe-moments and Eu2+ spins order, we have studied the doping dependence of T SDW and Eu2+ antiferromagnetic order T N . It is found that T SDW increases almost linearly with increasing x and remains nearly unchanged above x ∼ 0.4, whereas T N first appears at x ∼ 0.4 and varies almost linearly with further increasing x. These observations suggest that magnetic orders due to two sublattices are coupled to each other. The results discussed here are helpful for understanding the magnetic properties of ECFA and other iron-based superconductors.","PeriodicalId":119676,"journal":{"name":"Frontiers in Electronic Materials","volume":"336 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134530530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.3389/femat.2022.1046694
Cagri Oztan, Bengisu Şişik, Ryan Welch, S. LeBlanc
Additive manufacturing allows fabrication of custom-shaped thermoelectric materials while minimizing waste, reducing processing steps, and maximizing integration compared to conventional methods. Establishing the process-structure-property relationship of laser additive manufactured thermoelectric materials facilitates enhanced process control and thermoelectric performance. This research focuses on laser processing of bismuth telluride (Bi2Te3), a well-established thermoelectric material for low temperature applications. Single melt tracks under various parameters (laser power, scan speed and number of scans) were processed on Bi2Te3 powder compacts. A detailed analysis of the transition in the melting mode, grain growth, balling formation, and elemental composition is provided. Rapid melting and solidification of Bi2Te3 resulted in fine-grained microstructure with preferential grain growth along the direction of the temperature gradient. Experimental results were corroborated with simulations for melt pool dimensions as well as grain morphology transitions resulting from the relationship between temperature gradient and solidification rate. Samples processed at 25 W, 350 mm/s with 5 scans resulted in minimized balling and porosity, along with columnar grains having a high density of dislocations.
{"title":"Process-microstructure relationship of laser processed thermoelectric material Bi2Te3","authors":"Cagri Oztan, Bengisu Şişik, Ryan Welch, S. LeBlanc","doi":"10.3389/femat.2022.1046694","DOIUrl":"https://doi.org/10.3389/femat.2022.1046694","url":null,"abstract":"Additive manufacturing allows fabrication of custom-shaped thermoelectric materials while minimizing waste, reducing processing steps, and maximizing integration compared to conventional methods. Establishing the process-structure-property relationship of laser additive manufactured thermoelectric materials facilitates enhanced process control and thermoelectric performance. This research focuses on laser processing of bismuth telluride (Bi2Te3), a well-established thermoelectric material for low temperature applications. Single melt tracks under various parameters (laser power, scan speed and number of scans) were processed on Bi2Te3 powder compacts. A detailed analysis of the transition in the melting mode, grain growth, balling formation, and elemental composition is provided. Rapid melting and solidification of Bi2Te3 resulted in fine-grained microstructure with preferential grain growth along the direction of the temperature gradient. Experimental results were corroborated with simulations for melt pool dimensions as well as grain morphology transitions resulting from the relationship between temperature gradient and solidification rate. Samples processed at 25 W, 350 mm/s with 5 scans resulted in minimized balling and porosity, along with columnar grains having a high density of dislocations.","PeriodicalId":119676,"journal":{"name":"Frontiers in Electronic Materials","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130653713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.3389/femat.2022.1059684
F. A. Bipasha, L. C. Gomes, Jiaxing Qu, E. Ertekin
High-performance thermoelectric (TE) materials rely on semiconductors with suitable intrinsic properties for which carrier concentrations can be controlled and optimized. To demonstrate the insights that can be gained in computational analysis when both intrinsic properties and dopability are considered in tandem, we combine the prediction of TE quality factor (intrinsic properties) with first-principles simulations of native defects and carrier concentrations for the binary Sn chalcogenides SnS, SnSe, and SnTe. The computational predictions are compared to a comprehensive data set of previously reported TE figures-of-merit for each material, for both p-type and n-type carriers. The combined analysis reveals that dopability limits constrain the TE performance of each Sn chalcogenide in a distinct way. In SnS, TE performance for both p-type and n-type carriers is hindered by low carrier concentrations, and improved performance is possible only if higher carrier concentrations can be achieved by suitable extrinsic dopants. For SnSe, the p-type performance of the Cmcm phase appears to have reached its theoretical potential, while improvements in n-type performance may be possible through tuning of electron carrier concentrations in the Pnma phase. Meanwhile, assessment of the defect chemistry of SnTe reveals that p-type TE performance is limited by, and n-type performance is not possible due to, the material’s degenerate p-type nature. This analysis highlights the benefits of accounting for both intrinsic and extrinsic properties in a computation-guided search, an approach that can be applied across diverse sets of semiconductor materials for TE applications.
{"title":"Intrinsic properties and dopability effects on the thermoelectric performance of binary Sn chalcogenides from first principles","authors":"F. A. Bipasha, L. C. Gomes, Jiaxing Qu, E. Ertekin","doi":"10.3389/femat.2022.1059684","DOIUrl":"https://doi.org/10.3389/femat.2022.1059684","url":null,"abstract":"High-performance thermoelectric (TE) materials rely on semiconductors with suitable intrinsic properties for which carrier concentrations can be controlled and optimized. To demonstrate the insights that can be gained in computational analysis when both intrinsic properties and dopability are considered in tandem, we combine the prediction of TE quality factor (intrinsic properties) with first-principles simulations of native defects and carrier concentrations for the binary Sn chalcogenides SnS, SnSe, and SnTe. The computational predictions are compared to a comprehensive data set of previously reported TE figures-of-merit for each material, for both p-type and n-type carriers. The combined analysis reveals that dopability limits constrain the TE performance of each Sn chalcogenide in a distinct way. In SnS, TE performance for both p-type and n-type carriers is hindered by low carrier concentrations, and improved performance is possible only if higher carrier concentrations can be achieved by suitable extrinsic dopants. For SnSe, the p-type performance of the Cmcm phase appears to have reached its theoretical potential, while improvements in n-type performance may be possible through tuning of electron carrier concentrations in the Pnma phase. Meanwhile, assessment of the defect chemistry of SnTe reveals that p-type TE performance is limited by, and n-type performance is not possible due to, the material’s degenerate p-type nature. This analysis highlights the benefits of accounting for both intrinsic and extrinsic properties in a computation-guided search, an approach that can be applied across diverse sets of semiconductor materials for TE applications.","PeriodicalId":119676,"journal":{"name":"Frontiers in Electronic Materials","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123596091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-14DOI: 10.3389/femat.2022.1000781
Haitao Yang, Wenbo Wu
Compliant and soft sensors that detect machinal deformations become prevalent in emerging soft robots for closed-loop feedback control. In contrast to conventional sensing applications, the stretchy body of the soft robot enables programmable actuating behaviors and automated manipulations across a wide strain range, which poses high requirements for the integrated sensors of customized sensor characteristics, high-throughput data processing, and timely decision-making. As various soft robotic sensors (strain, pressure, shear, etc.) meet similar challenges, in this perspective, we choose strain sensor as a representative example and summarize the latest advancement of strain sensor-integrated soft robotic design driven by machine learning techniques, including sensor materials optimization, sensor signal analyses, and in-sensor computing. These machine learning implementations greatly accelerate robot automation, reduce resource consumption, and expand the working scenarios of soft robots. We also discuss the prospects of fusing machine learning and soft sensing technology for creating next-generation intelligent soft robots.
{"title":"A review: Machine learning for strain sensor-integrated soft robots","authors":"Haitao Yang, Wenbo Wu","doi":"10.3389/femat.2022.1000781","DOIUrl":"https://doi.org/10.3389/femat.2022.1000781","url":null,"abstract":"Compliant and soft sensors that detect machinal deformations become prevalent in emerging soft robots for closed-loop feedback control. In contrast to conventional sensing applications, the stretchy body of the soft robot enables programmable actuating behaviors and automated manipulations across a wide strain range, which poses high requirements for the integrated sensors of customized sensor characteristics, high-throughput data processing, and timely decision-making. As various soft robotic sensors (strain, pressure, shear, etc.) meet similar challenges, in this perspective, we choose strain sensor as a representative example and summarize the latest advancement of strain sensor-integrated soft robotic design driven by machine learning techniques, including sensor materials optimization, sensor signal analyses, and in-sensor computing. These machine learning implementations greatly accelerate robot automation, reduce resource consumption, and expand the working scenarios of soft robots. We also discuss the prospects of fusing machine learning and soft sensing technology for creating next-generation intelligent soft robots.","PeriodicalId":119676,"journal":{"name":"Frontiers in Electronic Materials","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133350922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-07DOI: 10.3389/femat.2022.1023415
Ali Ghadami, H. R. Mirdamadi, H. Khanbareh
With recent advances in system integration technologies, numerous efforts have been made to develop soft piezoelectric sensors for various engineering and healthcare applications. Using flexible and sensitive materials is crucial for designing soft sensors in order to maximize their efficiency and integrability. Micro-porous PU-PZT composite is a recently designed piezoelectric particulate composite material with an improved flexibility and piezoelectric voltage coefficient over common piezoelectric ceramics that makes it a promising candidate for application in soft sensors. In this study, we investigate the dynamic response and sensitivity of the micro-porous PU-PZT composite for applications in soft sensors in both 33 and 31 modes using energy methods. By using the effective field method, the micro-porous PU-PZT composite material properties were extracted and optimized based on the partially experimentally measured properties in order to get a complete picture of the properties of the material. In addition, the effects of changing the sensor geometry by varying the thickness and adding an extra layer between the piezoelectric layers are studied. Finally, a large area sensor based on micro-porous PU-PZT composite is simulated in finite element software, and the effect of several parameters on sensor’s performance is investigated. Dynamic analysis of the sensor shows high sensitivity in both 31 and 33 modes which is a significant improvement compared to the commonly used bulk piezoelectric ceramics. This work has demonstrated that due to the high output voltage and structural flexibility of the micro-porous PU-PZT composite, a flexible large-area sensor would be a suitable choice for artificial skins and smart gloves.
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