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1. (Digital Presentation) Multimodal Encapsulation of p-SnOx to Engineer the Carrier Density for Thin Film Transistor Applications

   https://doi.org/10.1149/MA2022-0215821mtgabs  

   Lee, Dong Hun ; Zhang, Yuxuan ; No, Kwangsoo ; Song, Han Wook ; Lee, Sunghwan ( October 2022 , ECS Meeting Abstracts)

   It has been challenging to synthesize p-type SnO_x(1≤x<2) and engineer the electrical properties such as carrier density and mobility due to the narrow processing window and the localized oxygen 2p orbitals near the valence band.

   We recently reported on the processing of p-type SnOx and an oxide-based p-n heterostructures, demonstrating high on/off rectification ratio (>10³), small turn-on voltage (<0.5 V), and low saturation current (~1×10^-10A)¹. In order to further understand the p-type oxide and engineer the properties for various electronic device applications, it is important to identify (or establish) the dominating doping and transport mechanisms. The low dopability in p-type SnOx, of which the causation is also closely related to the narrow processing window, needs to be mitigated so that the electrical properties of the material are to be adequately engineered^{2, 3}.

   Herein, we report on the multifunctional encapsulation of p-SnO_xto limit the surface adsorption of oxygen and selectively permeate hydrogen into the p-SnO_xchannel for thin film transistor (TFT) applications. Time-of-flight secondary ion mass spectrometry measurements identified that ultra-thin SiO₂as a multifunctional encapsulation layer effectively suppressed the oxygen adsorption on the back channel surface of p-SnO_xand augmented hydrogen density across the entire thickness of the channel. Encapsulated p-SnO_x-based TFTs demonstrated much-enhanced channel conductance modulation in response to the gate bias applied, featuring higher on-state current and lower off-state current. The relevance between the TFT performance and the effects of oxygen suppression and hydrogen permeation is discussed in regard to the intrinsic and extrinsic doping mechanisms. These results are supported by density-functional-theory calculations.

   Acknowledgement

   This work was supported by the U.S. National Science Foundation (NSF) Award No. ECCS-1931088. S.L. and H.W.S. acknowledge the support from the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 20011028) by KRISS. K.N. was supported by Basic Science Research Program (NRF-2021R11A1A01051246) through the NRF Korea funded by the Ministry of Education.

   References

   Lee, D. H.; Park, H.; Clevenger, M.; Kim, H.; Kim, C. S.; Liu, M.; Kim, G.; Song, H. W.; No, K.; Kim, S. Y.; Ko, D.-K.; Lucietto, A.; Park, H.; Lee, S., High-Performance Oxide-Based p–n Heterojunctions Integrating p-SnOx and n-InGaZnO.ACS Applied Materials & Interfaces2021,13(46), 55676-55686.

   Hautier, G.; Miglio, A.; Ceder, G.; Rignanese, G.-M.; Gonze, X., Identification and design principles of low hole effective mass p-type transparent conducting oxides.Nat Commun2013,4.

   Yim, K.; Youn, Y.; Lee, M.; Yoo, D.; Lee, J.; Cho, S. H.; Han, S., Computational discovery of p-type transparent oxide semiconductors using hydrogen descriptor.npj Computational Materials2018,4(1), 17.

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2. (Digital Presentation) Oxide Memristors Based on SiO 2 with Cu/Ag Alloy Metallization for Neuromorphic Computing

   https://doi.org/10.1149/MA2022-0215808mtgabs  

   Qin, Fei ; Song, Han Wook ; Qin, Fei ( October 2022 , ECS Meeting Abstracts)

   By mimicking biomimetic synaptic processes, the success of artificial intelligence (AI) has been astounding with various applications such as driving automation, big data analysis, and natural-language processing.[1-4] Due to a large quantity of data transmission between the separated memory unit and the logic unit, the classical computing system with von Neumann architecture consumes excessive energy and has a significant processing delay.[5] Furthermore, the speed difference between the two units also causes extra delay, which is referred to as the memory wall.[6, 7] To keep pace with the rapid growth of AI applications, enhanced hardware systems that particularly feature an energy-efficient and high-speed hardware system need to be secured. The novel neuromorphic computing system, an in-memory architecture with low power consumption, has been suggested as an alternative to the conventional system. Memristors with analog-type resistive switching behavior are a promising candidate for implementing the neuromorphic computing system since the devices can modulate the conductance with cycles that act as synaptic weights to process input signals and store information.[8, 9]

   The memristor has sparked tremendous interest due to its simple two-terminal structure, including top electrode (TE), bottom electrode (BE), and an intermediate resistive switching (RS) layer. Many oxide materials, including HfO₂, Ta₂O₅, and IGZO, have extensively been studied as an RS layer of memristors. Silicon dioxide (SiO₂) features 3D structural conformity with the conventional CMOS technology and high wafer-scale homogeneity, which has benefited modern microelectronic devices as dielectric and/or passivation layers. Therefore, the use of SiO₂as a memristor RS layer for neuromorphic computing is expected to be compatible with current Si technology with minimal processing and material-related complexities.

   In this work, we proposed SiO₂-based memristor and investigated switching behaviors metallized with different reduction potentials by applying pure Cu and Ag, and their alloys with varied ratios. Heavily doped p-type silicon was chosen as BE in order to exclude any effects of the BE ions on the memristor performance. We previously reported that the selection of TE is crucial for achieving a high memory window and stable switching performance. According to the study which compares the roles of Cu (switching stabilizer) and Ag (large switching window performer) TEs for oxide memristors, we have selected the TE materials and their alloys to engineer the SiO₂-based memristor characteristics. The Ag TE leads to a larger memory window of the SiO₂memristor, but the device shows relatively large variation and less reliability. On the other hand, the Cu TE device presents uniform gradual switching behavior which is in line with our previous report that Cu can be served as a stabilizer, but with small on/off ratio.[9] These distinct performances with Cu and Ag metallization leads us to utilize a Cu/Ag alloy as the TE. Various compositions of Cu/Ag were examined for the optimization of the memristor TEs. With a Cu/Ag alloying TE with optimized ratio, our SiO₂based memristor demonstrates uniform switching behavior and memory window for analog switching applications. Also, it shows ideal potentiation and depression synaptic behavior under the positive/negative spikes (pulse train).

   In conclusion, the SiO₂memristors with different metallization were established. To tune the property of RS layer, the sputtering conditions of RS were varied. To investigate the influence of TE selections on switching performance of memristor, we integrated Cu, Ag and Cu/Ag alloy as TEs and compared the switch characteristics. Our encouraging results clearly demonstrate that SiO₂with Cu/Ag is a promising memristor device with synaptic switching behavior in neuromorphic computing applications.

   Acknowledgement

   This work was supported by the U.S. National Science Foundation (NSF) Award No. ECCS-1931088. S.L. and H.W.S. acknowledge the support from the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 22011044) by KRISS.

   References

   [1] Younget al.,IEEE Computational Intelligence Magazine,vol. 13, no. 3, pp. 55-75, 2018.

   [2] Hadsellet al.,Journal of Field Robotics,vol. 26, no. 2, pp. 120-144, 2009.

   [3] Najafabadiet al.,Journal of Big Data,vol. 2, no. 1, p. 1, 2015.

   [4] Zhaoet al.,Applied Physics Reviews,vol. 7, no. 1, 2020.

   [5] Zidanet al.,Nature Electronics,vol. 1, no. 1, pp. 22-29, 2018.

   [6] Wulfet al.,SIGARCH Comput. Archit. News,vol. 23, no. 1, pp. 20–24, 1995.

   [7] Wilkes,SIGARCH Comput. Archit. News,vol. 23, no. 4, pp. 4–6, 1995.

   [8] Ielminiet al.,Nature Electronics,vol. 1, no. 6, pp. 333-343, 2018.

   [9] Changet al.,Nano Letters,vol. 10, no. 4, pp. 1297-1301, 2010.

   [10] Qinet al., Physica Status Solidi (RRL) - Rapid Research Letters, pssr.202200075R1, In press, 2022.

    

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3. The effect of intrinsic layer on the performance of oxide-based p-i-n heterojunctions integrating p-SnOx and n-InGaZnO

   Lee, Dong Hun ; Lee, Sunghwan ( May 2022 , Materials Research Society)

   The discovery of oxide electronics is of increasing importance today as one of the most promising new technologies and manufacturing processes for a variety of electronic and optoelectronic applications such as next-generation displays, batteries, solar cells, and photodetectors. The high potential use seen in oxide electronics is due primarily to their high carrier mobilities and their ability to be fabricated at low temperatures. However, since the majority of oxide semiconductors are n-type oxides, current applications are limited to unipolar devices, eventually developing oxide-based bipolar devices such as p-n diodes and complementary metal-oxide semiconductors. We have contributed to wide range of oxide semiconductors and their electronics and optoelectronic device applications. Particularly, we have demonstrated n-type oxide-based thin film transistors (TFT), integrating In2O3-based n-type oxide semiconductors from binary cation materials to ternary cation species including InZnO, InGaZnO (IGZO), and InAlZnO. We have suggested channel/metallization contact strategies to achieve stable TFT performance, identified vacancy-based native defect doping mechanisms, suggested interfacial buffer layers to promote charge injection capability, and established the role of third cation species on the carrier generation and carrier transport. More recently, we have reported facile manufacturing of p-type SnOx through reactive magnetron sputtering from a Sn metal target. The fabricated p-SnOx was found to be devoid of metallic phase of Sn from x-ray photoelectron spectroscopy and demonstrated stable performance in a fully oxide based p-n heterojunction together with n-InGaZnO. The oxide-based p-n junctions exhibited a high rectification ratio greater than 103 at ±3 V, a low saturation current of ~2x10-10, and a small turn-on voltage of -0.5 V. With all the previous achievements and investigations about p-type oxide semiconductors, challenges remain for implementing p-type oxide realization. For the implementation of oxide-based p-n heterojunctions, the performance needs to be further enhanced. The current on/off ration may be limited, in our device structure, due to either high reverse saturation current (or current density) or non-ideal performance. In this study, two rational strategies are suggested to introduce an “intrinsic” layer, which is expected to reduce the reverse saturation current between p-SnOx and n-IGZO and hence increase the on/off ratio. The carrier density of n-IGZO is engineered in-situ during the sputtering process, by which compositionally homogeneous IGZO with significantly reduced carrier density is formed at the interface. Then, higher carrier density IGZO is formed continuously on the lower carrier density IGZO during the sputtering process without any exposure of the sample to the air. Alternatively, heterogeneous oxides of MgO and SiO2 are integrated into between p-SnOx and n-IGZO, by which the defects on the surface can be passivated. The interfacial properties are thoroughly investigated using transmission electron microscopy and atomic force microscopy. The I-V characteristics are compared between the set of devices integrated with two types of “intrinsic” layers. The current research results are expected to contribute to the development of p-type oxides and their industrial application manufacturing process that meets current processing requirements, such as mass production in p-type oxide semiconductors. 

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4. Strategies to Enhance the Capability of Carrier Injection to the Effective Channel for Bottom-gated Amorphous Oxide Thin Films Transistors

   Liu, Mingyuan ; Kim, Hyeonghun ; Song, Han Wook ; Lee, Sunghwan ( November 2021 , Materials Research Society)

   Over the two decades, amorphous oxide semiconductors (AOSs) and their thin film transistor (TFT) channel application have been intensely explored to realize high performance, transparent and flexible displays due to their high field effect mobility (μFE=5-20 cm2/Vs), visible range optical transparency, and low temperature processability (25-300 °C).[1-2] The metastable amorphous phase is to be maintained during operation by the addition of Zn and additional third cation species (e.g., Ga, Hf, or Al) as an amorphous phase stabilizer.[3-5] To limit TFT off-state currents, a thin channel layer (10-20 nm) was employed for InZnO (IZO)-based TFTs, or third cations were added to suppress carrier generations in the TFT channel. To resolve bias stress-induced instabilities in TFT performance, approaches to employ defect passivation layers or enhance channel/dielectric interfacial compatibility were demonstrated.[6-7] Metallization contact is also a dominating factor that determines the performance of TFTs. Particularly, it has been reported that high electrical contact resistance significantly sacrifices drain bias applied to the channel, which leads to undesirable power loss during TFT operation and issues for the measurement of TFT field effect mobilities. [2, 8] However, only a few reports that suggest strategies to enhance contact behaviors are available in the literature. Furthermore, the previous approaches (1) require an additional fabrication complexity due to the use of additional treatments at relatively harsh conditions such as UV, plasma, or high temperatures, and (2) may lead to adverse effects on the channel material attributed to the chemical incompatibility between dissimilar materials, and exposures to harsh environments. Therefore, a simple and easy but effective buffer strategy, which does not require any additional process complexities and not sacrifice chemical compatibility, needs to be established to mitigate the contact issues and therefore achieve high performance and low power consumption AOS TFTs. The present study aims to demonstrate an approach utilizing an interfacial buffer layer, which is compositionally homogeneous to the channel to better align work functions between channel and metallization without a significant fabrication complexity and harsh treatment conditions. Photoelectron spectroscopic measurements reveal that the conducting IZO buffer, of which the work function (Φ) is 4.37 eV, relaxes a relatively large Φ difference between channel IZO (Φ=4.81 eV) and Ti (Φ=4.2-4.3 eV) metallization. The buffer is found to lower the energy barrier for charge carriers at the source to reach the effective channel region near the dielectric. In addition, the higher carrier density of the buffer and favorable chemical compatibility with the channel (compositionally the same) further contribute to a significant reduction in specific contact resistance as much as more than 2.5 orders of magnitude. The improved contact and carrier supply performance from the source to the channel lead to an enhanced field effect mobility of up to 56.49 cm2/Vs and a threshold voltage of 1.18 V, compared to 13.41 cm2/Vs and 7.44 V of IZO TFTs without a buffer. The present work is unique in that an approach to lower the potential barrier between the source and the effective channel region (located near the channel/dielectric interface, behaving similar to a buried-channel MOSFET [9]) by introducing a contact buffer layer that enhances the field effect mobility and facilitates carrier supply from the source to the effective channel region. 

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5. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 

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