Search Results
Impact of the precursor chemistry and process conditions on the cell-to-cell variability in 1T-1R based HfO2 RRAM devices
The Resistive RAM (RRAM) technology is currently in a level of maturity that calls for its integration into CMOS compatible memory arrays. This CMOS integration requires a perfect understanding of the cells performance and reliability in relation to the deposition processes used for their manufacturing. In this paper, the impact of the precursor chemistries and process conditions on the performance of HfO2 based memristive cells is studied. An extensive characterization of HfO2 based 1T1R cells, a comparison of the cell-to-cell variability, and reliability study is performed. The cells’ behaviors during forming, set, and reset operations are monitored in order to relate their features to conductive filament properties and process-induced variability of the switching parameters. The modeling of the high resistance state (HRS) is performed by applying the Quantum-Point Contact model to assess the link between the deposition condition and the precursor chemistry with the resulting physical cells characteristics.
Dielectrophoretic Immobilization of Yeast Cells Using CMOS Integrated Microfluidics
This paper presents a dielectrophoretic system for the immobilization and separation of live and dead cells. Dielectrophoresis (DEP) is a promising and efficient investigation technique for the development of novel lab-on-a-chip devices, which characterizes cells or particles based on their intrinsic and physical properties. Using this method, specific cells can be isolated from their medium carrier or the mixture of cell suspensions (e.g., separation of viable cells from non-viable cells). Main advantages of this method, which makes it favorable for disease (blood) analysis and diagnostic applications are, the preservation of the cell properties during measurements, label-free cell identification, and low set up cost. In this study, we validated the capability of complementary metal-oxide-semiconductor (CMOS) integrated microfluidic devices for the manipulation and characterization of live and dead yeast cells using dielectrophoretic forces. This approach successfully trapped live yeast cells and purified them from dead cells. Numerical simulations based on a two-layer model for yeast cells flowing in the channel were used to predict the trajectories of the cells with respect to their dielectric properties, varying excitation voltage, and frequency.
Through the Window: Exploitation and Countermeasures of the ESP32 Register Window Overflow â€
With the increasing popularity of IoT (Internet-of-Things) devices, their security becomes an increasingly important issue. Buffer overflow vulnerabilities have been known for decades, but are still relevant, especially for embedded devices where certain security measures cannot be implemented due to hardware restrictions or simply due to their impact on performance. Therefore, many buffer overflow detection mechanisms check for overflows only before critical data are used. All data that an attacker could use for his own purposes can be considered critical. It is, therefore, essential that all critical data are checked between writing a buffer and its usage. This paper presents a vulnerability of the ESP32 microcontroller, used in millions of IoT devices, that is based on a pointer that is not protected by classic buffer overflow detection mechanisms such as Stack Canaries or Shadow Stacks. This paper discusses the implications of vulnerability and presents mitigation techniques, including a patch, that fixes the vulnerability. The overhead of the patch is evaluated using simulation as well as an ESP32-WROVER-E development board. We showed that, in the simulation with 32 general-purpose registers, the overhead for the CoreMark benchmark ranges between 0.1% and 0.4%. On the ESP32, which uses an Xtensa LX6 core with 64 general-purpose registers, the overhead went down to below 0.01%. A worst-case scenario, modeled by a synthetic benchmark, showed overheads up to 9.68%.
Resilience in the Cyberworld: Definitions, Features and Models
Resilience is a feature that is gaining more and more attention in computer science and computer engineering. However, the definition of resilience for the cyber landscape, especially embedded systems, is not yet clear. This paper discusses definitions provided by different authors, on different years and with different application areas the field of computer science/computer engineering. We identify the core statements that are more or less common to the majority of the definitions, and based on this we give a holistic definition using attributes for (cyber-) resilience. In order to pave a way towards resilience engineering, we discuss a theoretical model of the life cycle of a (cyber-) resilient system that consists of key actions presented in the literature. We adapt this model for embedded (cyber-) resilient systems.
Toward Reliable Multi-Level Operation in RRAM Arrays: Improving Post-Algorithm Stability and Assessing Endurance/Data Retention
Achieving a reliable multi-level operation in resistive random access memory (RRAM) arrays is currently a challenging task due to several threats like the post-algorithm instability occurring after the levels placement, the limited endurance, and the poor data retention capabilities at high temperature. In this paper, we introduced a multi-level variation of the state-of-the-art incremental step pulse with verify algorithm (M-ISPVA) to improve the stability of the low resistive state levels. This algorithm introduces for the first time the proper combination of current compliance control and program/verify paradigms. The validation of the algorithm for forming and set operations has been performed on 4-kbit RRAM arrays. In addition, we assessed the endurance and the high temperature multi-level retention capabilities after the algorithm application proving a 1 k switching cycles stability and a ten years retention target with temperatures below 100 °C.
In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools
Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease and a major cause of morbidity and mortality worldwide. Although a curative therapy has yet to be found, permanent monitoring of biomarkers that refiect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fiuids like saliva is a promising approach for staging disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. However, the concurrent consideration of patients' demographic and medical parameters is necessary for achieving accurate outcomes. Therefore, Machine Learning (ML) tools can play an important role for analyzing patient data and providing comprehensive results for the recognition of COPD in a PoC setting. As a result, the objective of this research work was to implement ML tools on data acquired from characterizing saliva samples of COPD patients and healthy controls as well as their demographic information for PoC recognition of the disease. For this purpose, a permittivity biosensor was used to characterize dielectric properties of saliva samples and, subsequently, ML tools were applied on the acquired data for classification. The XGBoost gradient boosting algorithm provided a high classification accuracy and sensitivity of 91.25% and 100%, respectively, making it a promising model for COPD evaluation. Integration of this model on a neuromorphic chip, in the future, will enable the real-time assessment of COPD in PoC, with low cost, low energy consumption, and high patient privacy. In addition, constant monitoring of COPD in a near-patient setup will enable the better management of the disease exacerbations.