High-level Processing Research Articles

Fast FPGA-Based Image Feature Extraction for Data Fusion in Autonomous Vehicles.

Computer vision plays a critical role in many applications, particularly in the domain of autonomous vehicles. To achieve high-level image processing tasks such as image classification and object tracking, it is essential to extract low-level features from the image data. However, in order to integrate these compute-intensive tasks into a control loop, they must be completed as quickly as possible. This paper presents a novel FPGA-based system for fast and accurate image feature extraction, specifically designed to meet the constraints of data fusion in autonomous vehicles. The system computes a set of generic statistical image features, including contrast, homogeneity, and entropy, and is implemented on two Xilinx FPGA platforms - an Alveo U200 Data Center Accelerator Card and a Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit. Experimental results show that the proposed system achieves high-speed image feature extraction with low latency, making it well-suited for use in autonomous vehicle systems that require real-time image processing. The presented system can also be easily extended to extract additional features for various image and data fusion applications.

Data fabric and digital twins: An integrated approach for data fusion design and evaluation of pervasive systems

Recent technological improvements have made it possible for pervasive computing intelligent environments, augmented by sensors and actuators, to offer services that support society's aims for a wide variety of applications. This requires the fusion of data gathered from multiple sensors to convert them into information to obtain valuable knowledge. Poor implementation of data fusion hinders the appropriate actions from being taken and offering the appropriate support to users and environment needs, particularly relevant in the healthcare domain. Data fusion poses challenges that are mainly related to the quality of the data or data sources, the definition of a data fusion process and evaluating the data fusion carried out. There is also a lack of holistic engineering frameworks to address these challenges. These frameworks should be able to support automated methods of extracting knowledge from information, selecting algorithms and techniques, assessing information and evaluating information fusion systems in an automatic and standardized manner. This work proposes a holistic framework to improve data fusion in pervasive systems, addressing the issues identified by means of two processes: the first of which guides the design of the system architecture and focuses on data management. It is based on a previous proposal that integrated aspects of Data Fabric and Digital Twins to solve data management and data contextualization and representation issues, respectively. The extension of the previous proposal presented here was mainly defined by integrating aspects and techniques from different well-known multi-sensor data fusion models. The previous proposal identified high-level data processing activities and was intended to facilitate their traceability to components in the system architecture. However, the previously defined stages are not completely adequate in a data fusion process and the data processing tasks to be performed in each stage are not described in detail, especially in the data fusion stages. The second process of the framework deals with evaluating data fusion systems and is based on international standards to ensure the quality of the data fusion tasks performed by such systems. This process also offers guidelines for designing the architecture of an evaluation subsystem to automatically perform data fusion evaluation in runtime as part of the system. To illustrate the proposal, a system for preventing the spread of COVID-19 in nursing homes is described that was developed using the proposed guidelines It is also illustrated by a description of how the data fusion tasks it supports are evaluated by the proposed evaluation process. The overall evaluation of the data fusion performed by this system was considered satisfactory, which indicates that the proposal facilitates the design and development of data fusion systems and helps to achieve the necessary quality requirements.

26 To 'Proxy’ or Not to 'Proxy’: The Differential Effects of Cognitive Reserve on Late-Life Neuropsychological Functioning

Objective:As the population continues to age at a rapid pace, an important question that has been raised by clinicians and researchers alike, is “how can we preserve our cognitive abilities as older adults?” Cognitive reserve (CR) is thought to account for inter-individual differences in the cognitive trajectories of older people experiencing similar levels of brain pathology. Being a hypothetical construct, CR cannot be measured directly and therefore, must be operationalized through a combination of demographic and lifestyle variables. While there is sufficient empirical evidence supporting the relationship between individual CR proxies and cognitive functioning, few studies to date have explored which CR proxy is most important in predicting aspects of late-life cognitive functioning and whether composite measure of CR accurately predict cognitive functioning, above and beyond variables traditionally associated with late-life cognitive ability (e.g., age, gender, cardiovascular risk, depression). The present cross-sectional study sought to examine the relationship between three well-established CR proxies—educational attainment, mental workplace demands, crystallized intelligence—and baseline neuropsychological functioning in a clinical sample of older adults without dementia.Participants and Methods:Using archival data from 248 older adult patients seen at geriatric specialist hospital in Ontario, Canada, we examined the cumulative and independent effects of educational attainment (years of formal education), mental workplace demands (mean analyst rating for 10 O*NET variables measuring cognitively complex work activities), and crystallized intelligence (score on WASI-I Vocabulary subtest) on performance across several neuropsychological tests assessing i.) verbal memory, ii.) visual memory, iii.) attention, concentration, and working memory, iv.) executive functioning, v.) visuospatial processing, and vi.) language. Composite scores for all six cognitive domains were calculated by averaging converted z-scores on domain-specific tests of neuropsychological functioning. A series of regression models were then constructed to evaluate the relationship between CR and late-life cognitive functioning. To determine the relative importance of each CR proxy, a follow-up relative weight analysis (RWA) was performed for each regression model.Results:After controlling for age, gender, cardiovascular risk, and depression, our composite measure of CR (average z-score of educational attainment, mental workplace demands, crystallized intelligence) proved to be a significant predictor across all domains of cognitive functioning. Of the six cognitive domains assessed, CR was the most important in predicting higher-order cognitive functions, such as working memory and executive functioning. Out of the three CR proxies, crystallized intelligence was the only CR proxy that significantly predicted performance across all six cognitive domains. RWA revealed that crystallized intelligence was the most important in predicting neuropsychological functioning, followed by educational attainment, and mental workplace demands.Conclusions:Despite being closely related, all three CR proxies demonstrated differential effects on late-life cognitive functioning. Overall, our findings suggest that the effects of CR are not generalizable across all cognitive domains and appears to be somewhat dependent on the CR proxy used. This study supports the existing literature in demonstrating the robustness of crystallized intelligence as a CR proxy and provides preliminary evidence supporting the idea that tasks that require higher levels of cognitive processing are the most influenced by CR.

Phonological discrimination and contrast detection in pupillometry.

The perception of phonemes is guided by both low-level acoustic cues and high-level linguistic context. However, differentiating between these two types of processing can be challenging. In this study, we explore the utility of pupillometry as a tool to investigate both low- and high-level processing of phonological stimuli, with a particular focus on its ability to capture novelty detection and cognitive processing during speech perception. Pupillometric traces were recorded from a sample of 22 Danish-speaking adults, with self-reported normal hearing, while performing two phonological-contrast perception tasks: a nonword discrimination task, which included minimal-pair combinations specific to the Danish language, and a nonword detection task involving the detection of phonologically modified words within sentences. The study explored the perception of contrasts in both unprocessed speech and degraded speech input, processed with a vocoder. No difference in peak pupil dilation was observed when the contrast occurred between two isolated nonwords in the nonword discrimination task. For unprocessed speech, higher peak pupil dilations were measured when phonologically modified words were detected within a sentence compared to sentences without the nonwords. For vocoded speech, higher peak pupil dilation was observed for sentence stimuli, but not for the isolated nonwords, although performance decreased similarly for both tasks. Our findings demonstrate the complexity of pupil dynamics in the presence of acoustic and phonological manipulation. Pupil responses seemed to reflect higher-level cognitive and lexical processing related to phonological perception rather than low-level perception of acoustic cues. However, the incorporation of multiple talkers in the stimuli, coupled with the relatively low task complexity, may have affected the pupil dilation.

FMRI fluctuations within the language network are correlated with severity of hallucinatory symptoms in schizophrenia

Although schizophrenia (SZ) represents a complex multiform psychiatric disorder, one of its most striking symptoms are auditory verbal hallucinations (AVH). While the neurophysiological origin of this pervasive symptom has been extensively studied, there is so far no consensus conclusion on the neural correlates of the vulnerability to hallucinate. With a network-based fMRI approach, following the hypothesis of altered hemispheric dominance (Crow, 1997), we expected that LN alterations might result in self-other distinction impairments in SZ patients, and lead to the distressing subjective experiences of hearing voices. We used the independent component analysis of resting-state fMRI data, to first analyze LN connectivity in three groups of participants: SZ patients with and without hallucinations (AVH/D+ and AVH/D–, respectively), and a matched healthy control (HC) group. Then, we assessed the fMRI fluctuations using additional analyses based on fractional Amplitude of Low Frequency-Fluctuations (fALFF), both at the network- and region of interest (ROI)-level. Specific LN nodes were recruited in the right hemisphere (insula and Broca homologous area) for AVH/D+ , but not for HC and AVH/D–, consistent with a left hemisphere deficit in AVH patients. The fALFF analysis at the ROI level showed a negative correlation between fALFF Slow-4 and P1 Delusions PANSS subscale and a positive correlation between the fALFF Slow-5 and P3 Hallucination PANSS subscale for AVH/D+ only. These effects were not a consequence of structural differences between groups, as morphometric analysis did not evidence any group differences. Given the role of language as an emerging property resulting from the integration of many high-level cognitive processes and the underlying cortical areas, our results suggest that LN features from fMRI connectivity and fluctuations can be a marker of neurophysiological features characterizing SZ patients depending on their vulnerability to hallucinate.

Sound category habituation requires task-relevant attention.

Processing the wealth of sensory information from the surrounding environment is a vital human function with the potential to develop learning, advance social interactions, and promote safety and well-being. To elucidate underlying processes governing these activities we measured neurophysiological responses to patterned stimulus sequences during a sound categorization task to evaluate attention effects on implicit learning, sound categorization, and speech perception. Using a unique experimental design, we uncoupled conceptual categorical effects from stimulus-specific effects by presenting categorical stimulus tokens that did not physically repeat. We found effects of implicit learning, categorical habituation, and a speech perception bias when the sounds were attended, and the listeners performed a categorization task (task-relevant). In contrast, there was no evidence of a speech perception bias, implicit learning of the structured sound sequence, or repetition suppression to repeated within-category sounds (no categorical habituation) when participants passively listened to the sounds and watched a silent closed-captioned video (task-irrelevant). No indication of category perception was demonstrated in the scalp-recorded brain components when participants were watching a movie and had no task with the sounds. These results demonstrate that attention is required to maintain category identification and expectations induced by a structured sequence when the conceptual information must be extracted from stimuli that are acoustically distinct. Taken together, these striking attention effects support the theoretical view that top-down control is required to initiate expectations for higher level cognitive processing.

Brain Representation of Animal and Non-Animal Images in Patients with Mild Cognitive Impairment and Alzheimer's Disease.

In early Alzheimer's disease (AD), high-level visual functions and processing speed are impacted. Few functional magnetic resonance imaging (fMRI) studies have investigated high-level visual deficits in AD, yet none have explored brain activity patterns during rapid animal/non-animal categorization tasks. To address this, we utilized the previously known Integrated Cognitive Assessment (ICA) to collect fMRI data and compare healthy controls (HC) to individuals with mild cognitive impairment (MCI) and mild AD. The ICA encompasses a rapid visual categorization task that involves distinguishing between animals and non-animals within natural scenes. To comprehensively explore variations in brain activity levels and patterns, we conducted both univariate and multivariate analyses of fMRI data. The ICA task elicited activation across a range of brain regions, encompassing the temporal, parietal, occipital, and frontal lobes. Univariate analysis, which compared responses to animal versus non-animal stimuli, showed no significant differences in the regions of interest (ROIs) across all groups, with the exception of the left anterior supramarginal gyrus in the HC group. In contrast, multivariate analysis revealed that in both HC and MCI groups, several regions could differentiate between animals and non-animals based on distinct patterns of activity. Notably, such differentiation was absent within the mild AD group. Our study highlights the ICA task's potential as a valuable cognitive assessment tool designed for MCI and AD. Additionally, our use of fMRI pattern analysis provides valuable insights into the complex changes in brain function associated with AD. This approach holds promise for enhancing our understanding of the disease's progression.

Modeling aircraft similarity with musical auditory feature extraction

The increased use of unmanned aircraft systems for drone delivery and air taxis means the psychological effects of these systems (annoyance and perception) must be considered. Though aircraft perception is frequently evaluated with simple level-based metrics, higher level cognitive processes such as classification, identification, and localization benefit from additional signal features. To explore other non-level metrics, a pair-wise similarity experiment with four categories of sounds (including non-aircraft, rotorcraft, jets, and propeller aircraft) was performed by a panel of participants. These similarity ratings were decomposed into a timbre space using a two dimensional multidimensional scaling (MDS) analysis. This approach provided groupings that served as the baseline data for machine learning models to extract dominant features required to categorize aircraft signals. To model human responses, a musical feature extraction toolbox was used to obtain a set of audio attributes from the signals. These features were passed through a random decision forest, coupled with recursive feature elimination. The strength of this model is that it uses a data-driven approach to determine the appropriate mixture of waveform (or envelope) and spectral attributes that best capture participants' judgments. The model was validated by introducing a set of signals not presented to the participants, which were successfully placed in the timbre space corresponding to the correct category.

Visual orientation discrimination skills are tightly linked with specific aspects of human intelligence.

We investigate the notion that basic visual information is acting as a building block for more complex cognitive processes in humans. Specifically, we measured individual visual orientation discrimination thresholds to report significant correlations against the total standardised intelligence quotient (IQ), verbal-IQ and non-verbal IQ scores evaluated using the Wechsler Abbreviated Scale of Intelligence Second Edition (WASI-II) test battery comprising Verbal Reasoning, Block Design, Similarities and Matrix Reasoning subtests (N = 92). A multiple linear regression analysis showed that participants' performance in our visual discrimination task, could be explained only by individual scores in Verbal Reasoning which quantifies the ability to comprehend and describe words and Matrix Reasoning, which evaluates general visual processing skills including abstract and spatial perception. Our results demonstrate that low-level visual abilities and high-level cognitive processes are more tightly interwoven together than previously thought and this result could pave the way for further research on how cognition can be defined by basic sensory processes.

Ambient-aware continuous aid for mountain rescue activities

Ambient-awareness in conjunction with pervasive computing is a significant challenge for system designers. It follows the necessity of gathering raw, massive and heterogeneous environmental data which we obtained, while middleware processes must merge context modelling and reasoning seamlessly. We proposed a system supporting mountain rescuers which is demanding due to the large number of environmental objects interacting, as well as high data variability. We presented complex context processing embedded in the proposed context life cycle and implemented it in a difficult mountain environment. We introduced five weather scenarios which are a basis for contextual and perceptual processing during the validation of our model. The system merges a message streaming broker for massive data transport, low and high-level processing algorithms, repositories and a logical SAT solver. It constitutes a Context-Aware-as-a-Service (CAaaS) system, offering advanced support for mountain rescue operations. The provided software model defines middleware components which act on a predicted context and transform in situ sensor data into smart decisions, and which could operate as a platform-based cloud computing model. It is an enabler yielding a synergy effect with different software components orchestration when providing pro-activeness and non-intrusiveness concerning smart decisions.

Glaucoma-associated abnormalities in cortical activity during a visuocognitive task

ObjectiveTo investigate neurophysiological dynamics during a visuocognitive task in glaucoma patients vs. healthy controls. MethodsFifteen patients with early-stage primary open-angle glaucoma (POAG) and fifteen age-matched healthy participants underwent a “go/no-go” task, monitored with EEG. Participants had to semantically categorize visual objects in central vision, with animal or furniture as targets according to the experimental block. ResultsEarly visual processing was delayed by 50 ms in patients with POAG compared to controls. The patients displayed a smaller difference between animal and furniture categorization during higher-level cognitive processing (at 400–600 ms). Regarding behavioral data, the groups differed in accuracy performance and decision criterion. As opposed to the control group, patients did not display facilitation and a higher accuracy rate for animal stimuli. However, patients maintained a consistent decision criterion throughout the experiment, whereas controls displayed a shift towards worse decision criteria in furniture trials, with higher error rate. ConclusionsThe comparative analysis of behavioral and neurophysiological data revealed in POAG patients a delay in early visual processing, and potential high-level cognitive compensation during late, task-dependent activations. SignificanceTo our knowledge, our findings provide the first evidence of modification in cognitive brain dynamics associated with POAG.

An Integrative Model of Youth Anxiety: Cognitive-Affective Processes and Parenting in Developmental Context.

Multiple theoretical frameworks have been proposed to provide a more comprehensive picture of the risk factors that influence anxiety-related developmental trajectories. Nonetheless, there remains a need for an integrative model that outlines: (1) which risk factors may be most pertinent at different points in development, and (2) how parenting may maintain, exacerbate, or attenuate an affective style that is characterized by high negative emotional reactivity to unfamiliar, uncertain, and threatening situations. A developmentally informed, integrative model has the potential to guide treatment development and delivery, which is critical to reducing the public health burden associated with these disorders. This paper outlines a model integrating research on many well-established risk mechanisms for anxiety disorders, focusing on (1) the developmental progression from emotional reactivity constructs early in life to those involving higher-level cognitive processes later in youth, and (2) potential pathways by which parenting may impact the stability of youth's cognitive-affective responses to threat-relevant information across development.

High-Level Hessian-Based Image Processing with the Frangi Neuron

The Frangi neuron proposed in this work is a complex element that allows high-level Hessian-based image processing. Its adaptive parameters (weights) can be trained using a minimum number of training data. In our experiment, we showed that just one image is enough to optimize the values of the weights. An intuitive application of the Frangi neuron is to use it in image segmentation process. In order to test the performance of the Frangi neuron, we used diverse medical datasets on which second-order structures are visualized. The Frangi network presented in this paper trained on a single image proved to be significantly more effective than the U-net trained on the same dataset. For the datasets tested, the network performed better as measured by area under the curve receiver operating characteristic (ROC AUC) than U-net and the Frangi algorithm. However, the Frangi network performed several times faster than the non-GPU implementation of Frangi. There is nothing to prevent the Frangi neuron from being used as part of any other network as a component to process two-dimensional images, for example, to detect certain second-order features in them.

Neurocomputations on dual-brain signals underlie interpersonal prediction during a natural conversation

Prediction on the partner's speech plays a key role in a smooth conversation. However, previous studies on this issue have been majorly conducted at the single-brain rather than dual-brain level, leaving the interpersonal prediction hypothesis untested. To fill this gap, this study combined a neurocomputational modeling approach with a natural conversation paradigm in which two salespersons persuaded a customer to buy their product with their haemodynamic signals being collected using functional near-infrared spectroscopy hyperscanning. First, the results showed a cognitive hierarchy in a natural conversation, with the lower-level process (i.e., pragmatic representation of the persuasion) in the salesperson interacting with the higher-level process (i.e., value representation of the product) in the customer. Next, we found that the right dorsal lateral prefrontal cortex (rdlPFC) and temporoparietal junction (rTPJ) were associated with the representation of the product's value in the customer, while the right inferior frontal cortex (rIFC) was associated with the representation of the pragmatic processes in the salesperson. Finally, neurocomputational modeling results supported the prediction of the salesperson's lower-level brain activity based on the customer's higher-level brain activity. Moreover, the updating weight of the prediction model based on the neural computation between the rIFC of the salesperson and the rTPJ of the customer was closely associated with the interaction context, whereas that based on the rIFC-rdlPFC was not. In summary, these findings provide initial support for the interpersonal prediction hypothesis at the dual-brain level and reveal a hierarchy for the interpersonal prediction process.

Cognitive and emotional processing in tasks with emotional valence: Analysis of age and gender role on child development variations

Cognitive control consists of high-level cognitive processes regulating thoughts and actions during goal-directed behavior and problem-solving. This study analyzes the performance of 4- to 8-year-old children in Stroop-like and ToL tasks using stimuli with different emotional valence. Significant differences were found in the performance in the congruent block of the Stroop-like task, where 5-year-old children presented a higher performance in the neutral condition. Also, a significant difference was only found in the incongruent block (with higher demand for inhibition), which indicates that girls performed better than boys in both task conditions. Variations in the Stroop-like task performance were found in preschoolers but not in older children, especially in girls than in boys. Specifically, these variations were found between age groups with at least two years of difference. No statistically significant differences were found in performance nor planning time in ToL between the age and gender groups in any of the task conditions. The findings highlight the need to analyze the interaction between cognitive and emotional processing, individual differences, and task demands.

Perception and Memory in the Ventral Visual Stream and Medial Temporal Lobe.

Perception and memory are traditionally thought of as separate cognitive functions, supported by distinct brain regions. The canonical perspective is that perceptual processing of visual information is supported by the ventral visual stream, whereas long-term declarative memory is supported by the medial temporal lobe. However, this modular framework cannot account for the increasingly large body of evidence that reveals a role for early visual areas in long-term recognition memory and a role for medial temporal lobe structures in high-level perceptual processing. In this article, we review relevant research conducted in humans, nonhuman primates, and rodents. We conclude that the evidence is largely inconsistent with theoretical proposals that draw sharp functional boundaries between perceptual and memory systems in the brain. Instead, the weight of the empirical findings is best captured by a representational-hierarchical model that emphasizes differences in content, rather than in cognitive processes within the ventral visual stream and medial temporal lobe.

DES and IIoT fusion approach towards real-time synchronization of physical and digital components in manufacturing processes

Today's manufacturing systems offer more products to meet specific needs. Complex production systems in rapidly changing environments result from product variation, shorter product life cycles, and supply chain expansion. A cyber-physical production system (CPPS) can use manufacturing and logistics data to plan, monitor, and control production. Discrete event simulation (DES) and digital twin (DT) technology can model and evaluate manufacturing and logistics processes using high-level decision support and process monitoring. The cost of collecting input data from different enterprise data sources and mapping it into models and the lack of qualified experts prevent the widespread use of these methods in industry, especially in small and medium-sized enterprises and larger multinational companies. This research aims to create a modular digital twin framework for manufacturing process optimization and real-time monitoring in an industrial environment with few components. The system can identify and track the product through the manufacturing cycle while updating the DT in real-time and can be used independently to collect input parameters for discrete event-driven simulations and even for automatic simulation building in the future. The framework's operation will be shown through an example. With the proposed IIoT (industrial internet of things) system integration, it can detect faults and warn of deviations from normal operation, and DT can drastically reduce data collection and model building and support model reusability, increasing sustainability.

Hand-blink reflex modulation: The role of primary emotions and attachment dimensions.

The hand-blink reflex (HBR) is a subcortical response elicited by the electrical stimulation of the median nerve. HBR magnitude is enhanced when the stimulated hand is close to the face and is modulated by high-level structures according to the perceived threat magnitude. Psychological factors may contribute to threat evaluation and possibly to HBR amplitude modulation. In this study, we assessed distinctively emotional and relational aspects of personality and evaluated their associations with the HBR response, or lack thereof, in healthy subjects. Seventy-one volunteers filled the Experiences in Close Relationships Scale, the Affective Neuroscience Personality Scales, and the State-Trait Anxiety Inventory Form Y questionnaires and underwent HBR recording. We found that the HBR could be evoked only in 50.7% of all subjects (responders). Non-responders subjects showed higher scores in the avoidance dimension (p = .005), and lower scores in the care dimension (p = .008), compared with responders. In responders, regression analysis showed a negative association of HBR amplitude (difference in near vs. far responses) with anger dimension and a positive association with state anxiety (R2 = 0.239). A positive association also emerged with HBR latency and fear dimension (R2 = 0.419). We conclude that primary emotional and relational factors may play an important role in the modulation of brainstem circuits mediating the HBR response. Our results may also contribute to the question about the absence of the HBR in about half of the subjects since high-level cognitive processes seem to play an important role in the differentiation between responder and non-responder.

Corrosion evaluation for engineered barriers in crystalline host rock – challenges for container development in the German site selection process

Abstract. Crystalline rock is being considered as a potential host rock for a deep geological disposal facility for high-level radioactive waste in the ongoing site selection process in Germany. As crystalline rock is commonly highly fractured, the designation of a containment-providing rock zone is potentially not possible. In this case, according to German law (§ 23 (1) and (4) StandAG), the safe containment of the nuclear waste for 1 million years must be guaranteed by engineered and geo-engineered barriers. Therefore, it is of great importance to evaluate and understand processes that might lead to corrosion of the canister from the outside or from the inside. The long-term integrity of the waste canister depends on various parameters such as the material, microstructure, and coating of the canister; the properties of the geo-engineered barriers; the local pore water composition; the radiolysis on the inside of the container; and the tectonic strain. In a recent project (Long-Term Integrity of Canisters in Crystalline Host Rock – LaKris), the challenges of determining the durability of waste canisters in crystalline host rock have been addressed by comparing and evaluating different international concepts for canister design and discussing their applicability to the requirements in Germany. Further focus lies on the evaluation of predominant corrosion mechanisms while also considering the geochemical environment of the crystalline host rock in Germany that the canisters might be subjected to. This contribution will highlight the activities within the project with regard to evaluating the corrosion properties of different potential canister materials such as copper, cast iron, and stainless steel. The aim is to deepen the understanding of individual processes and their combined effects as well as their classification into favourable or unfavourable processes and conditions for the derivation or justification of requirements. It also includes a review of the existing requirements on canister integrity according to the regulatory framework as well as a discussion about important hydrogeochemical parameters, which could influence the corrosion of the canister, including the limited availability and uncertainty of such data.

Method for improving dry etching end point detection based on change in time accumulation correlation of plasma emitting wavelengths

This study proposes an optical emission spectroscopy (OES) analysis methodology to improve the ability to detect etching endpoints during high-level semiconductor plasma etching processes. Representative etching endpoint detection methods using single wavelength intensity or multiple wavelength intensity ratio changes include a low signal-to-noise ratio, high plasma instability, a small etching open area, and weak by-product emission signal problems due to deep etching under high-level process conditions such as high aspect ratio contact etching (HARC). As a result, it is difficult to detect the etching endpoint due to the very small or noisy change in the intensity over time due to the process progress of the wavelength selected by OES to detect the etching endpoint. In this study, a method of deriving an accumulative time correlation value according to process progress between selected wavelengths was developed by selecting all wavelengths observed in a specific wavelength region such as ultraviolet region in a spectrum emitted from plasma during a plasma etching process. After classifying the entire correlation signal groups derived as a pair of two intensity peak wavelengths into a dynamic time wrapping algorithm, the intensity change rate of the signal according to the process time was observed by selecting the signal with the most sensitive time change rate during the process. During the vertical nand flash memory manufacturing process, a test wafer for the purpose of detecting the etching endpoint was manufactured and evaluated under the conditions of the cell metal contact etching process, which is a high-level HARC etching process. As a result, it was confirmed that the signal selected by deriving the time accumulative correlation method had a high intensity change rate and a signal-to-noise ratio over time compared to a single wavelength or a plurality of wavelength ratio signals. The method proposed in this study is expected to contribute to process optimization by contributing to improving the ability to detect etching endpoints in high-level plasma etching processes in the future.