Read Noise Research Articles

Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain Sensing

Because of their high spatial resolution over extended lengths, distributed fiber optic sensors (DFOS) enable us to monitor a wide range of structural effects and offer great potential for diverse structural health monitoring (SHM) applications. However, even under controlled conditions, the useful signal in distributed strain sensing (DSS) data can be concealed by different types of measurement principle-related disturbances: strain reading anomalies (SRAs), dropouts, and noise. These disturbances can render the extraction of information for SHM difficult or even impossible. Hence, cleaning the raw measurement data in a pre-processing stage is key for successful subsequent data evaluation and damage detection on engineering structures. To improve the capabilities of pre-processing procedures tailored to DSS data, characteristics and common remediation approaches for SRAs, dropouts, and noise are discussed. Four advanced pre-processing algorithms (geometric threshold method (GTM), outlier-specific correction procedure (OSCP), sliding modified z-score (SMZS), and the cluster filter) are presented. An artificial but realistic benchmark data set simulating different measurement scenarios is used to discuss the features of these algorithms. A flexible and modular pre-processing workflow is implemented and made available with the algorithms. Dedicated algorithms should be used to detect and remove SRAs. GTM, OSCP, and SMZS show promising results, and the sliding average is inappropriate for this purpose. The preservation of crack-induced strain peaks’ tips is imperative for reliable crack monitoring.

Calibration and In-flight Performance of DART’s Didymos Reconnaissance and Asteroid Camera for OpNav (DRACO)

The Didymos Reconnaissance and Asteroid Camera for OpNav (DRACO) was the only instrument on board NASA’s Double Asteroid Redirection Test spacecraft. DRACO had a 2628.326 mm focal length Ritchey–Chrétien telescope with a 208.28 mm aperture and a 0.29° field of view (FOV). The camera used a front-side-illuminated complementary metal–oxide–semiconductor (CMOS) sensor, BAE CIS2521F, with 2048 × 2048 illuminated pixels. DRACO worked with an onboard image processor to support autonomous targeting and ensure impact with Dimorphos, the secondary asteroid in the 65803 Didymos system, on 2022 September 26. This paper covers the calibration and in-flight performance of DRACO, including descriptions of ground and in-flight activities, instrument characterization, the calibration pipeline, and delivered Planetary Data System (PDS) products. DRACO exhibited low read noise and dark current that enhanced the instrument’s sensitivity, although the sensor’s nonlinearity and popcorn noise in global shutter mode required careful characterization and compensation to optimize terminal navigation performance. DRACO had a relatively circular point-spread function of less than two binned pixels across the full FOV. The combined calibration efforts achieved an absolute radiometric calibration (in both rolling and global shutter modes) of better than 20% and a relative calibration across an image of <0.2%, despite no formal radiometric requirements. DRACO acquired 259,290 images in flight, which have been delivered to the PDS in both raw and calibrated forms. DRACO’s excellent performance indicates that similar off-the-shelf CMOS detectors would be well suited for future planetary missions in low-radiation environments.

Polarization Pruning: Reliability Enhancement of Hafnia-Based Ferroelectric Devices for Memory and Neuromorphic Computing.

Ferroelectric (FE) materials are key to advancing electronic devices owing to their non-volatile properties, rapid state-switching abilities, and low-energy consumption. FE-based devices are used in logic circuits, memory-storage devices, sensors, and in-memory computing. However, the primary challenge in advancing the practical applications of FE-based memory is its reliability. To address this problem, a novel polarization pruning (PP) method is proposed. The PP is designed to eliminate weakly polarized domains by applying an opposite-sign pulse immediately after a program or erase operation. Significant improvements in the reliability of ferroelectric devices are achieved by reducing the depolarization caused by weakly polarized domains and mitigating the fluctuations in the ferroelectric dipole. These enhancements include a 25% improvement in retention, a 50% reduction in read noise, a 45% decrease in threshold voltage variation, and a 72% improvement in linearity. The proposed PP method significantly improves the memory storage efficiency and performance of neuromorphic systems.

(Invited) Analog Computing with High Precision and Programmability Enabled by Memristors

While digital computing dominates the technological landscape, analog computing distinguishes itself with superior energy efficiency and high throughput. However, its historical limitation in precision and programmability has confined its application to specific and low-precision domains, notably in neural networks. The escalating challenge posed by the analog data deluge calls for versatile analog platforms. These platforms must not only exhibit exceptional efficiency but also boast reconfigurability and precision.Recent breakthroughs in analog devices, such as memristors, have laid the groundwork for unparalleled analog computing capabilities. Leveraging the multifaceted role of memristors, we introduce memristive field-programmable analog arrays (FPAAs), mirroring the functionality of their digital counterparts, field-programmable digital arrays (FPGAs). To elevate precision, we delve into the origins of reading noise, successfully mitigating it and achieving an unparalleled 2048 conductance levels in individual memristors—equivalent to 11 bits per cell, setting a record precision among diverse memory types. Acknowledging the persistent demand for single or double precision in various applications, we propose and develop a circuit architecture and programming protocol. This innovation enables analog memories to attain arbitrarily high precision with minimal circuit overhead. Our experimental validation involves a memristor System-on-Chip fabricated in a standard foundry, demonstrating significantly improved precision and power efficiency compared to traditional digital systems.The co-design approach presented empowers low-precision analog devices to perform high-precision computing within a programmable platform. This demonstration underscores the transformative potential of analog computing, transcending historical limitations and ushering in a new era of precision and efficiency.

Simulation based analysis of HK-Ge-Step-FinFET and its usage as inverter & SRAM

This paper deals with comparative simulation of High-k dielectrics -Germanium Step FinFET (HK-Ge-Step-FinFET) device with reference Step FinFET. For the first time we have investigated the impact of various dimensional parameters like oxide thickness tox, gate length Lg, drain bias voltage Vds on the performance of Proposed and Reference FinFET devices. These FinFET structures have been designed and simulated in Sentaurus TCAD and Cadence Virtuoso. The electrical parameters such as current ratio ION/IOFF, Sub-threshold Swing SS , Drain Induced Barrier Lowering (DIBL), threshold voltage Vth, gate capacitance, intrinsic delay and transconductance are extracted at 10 nm gate length. It is noticed that there is a significant improvement of 28 times and 23 times in ION for proposed device over reference FINFET at Vds = 1 V and Vds = 0.5 V respectively, improvement in ION/IOFF ratio from 8.05 × 108 to 6.65 × 1010, SS of 63.21 mV/decade to 61.5 mV/decade and excellent threshold voltage of 0.18 V in proposed FinFET. The characteristics of the proposed SRAM cell including, static noise margin (SNM), read/write delay, and subthreshold leakage power, are compared with the conventional 6 T SRAM cells. It is reported that the FinFET SRAM cell has RSNM, HSNM, and WNM of 285 mV, 360 mV, and 302 mV, respectively, at Vds = 1 V. Furthermore, the suggested device-based SRAM cell outperforms traditional SRAM cells at 1.0 V in terms of read noise margin, hold noise margin, and write noise margin, as well as leakage power. Thus, it may prove to be a viable option for lowering leakage components, making it effective for low-power and high-performance inverter and SRAM cell design in the nanoscale regime.

MDB-47. DEEP LEARNING DECODES NEW SITES OF RNA TRANSLATION IN MEDULLOBLASTOMA

Abstract BACKGROUND Medulloblastoma, a malignant brain cancer predominantly affecting children, possesses a high propensity for metastasis and is frequently fatal. Through studies on RNA translation, a core biological process, we aim to uncover the disease biology of cancer in order to design therapeutic targets. Yet, the exploration of canonical protein-coding genes has thus far yielded few viable targets in medulloblastoma, where non-canonical open reading frames (ORFs) are increasingly being investigated for their regulatory role. METHODS To address the significant challenge in delineating novel translated ORFs in individual cells or tissues, we have designed RIBO-former, a best-of-its-class machine learning model that leverages ribosome profiling data to detect translated open reading frames. Through RIBO-former, we expanded the proteome of healthy prenatal (n=30) and adult (n=42) samples, which are notoriously hard to process due to low read depth and noise, in addition to medulloblastoma cancer tissue (n=8) and cell line (n=15) data. RESULTS Across all medulloblastoma cell lines, we determine an initial selection of 3,638 non-canonical ORFs. Unsupervised clustering of the ribosome profiling read counts within these non-canonical ORF regions group samples according to the described subtypes of medulloblastoma cancer, hinting at the significant role these ncORFs play in protein homeostasis. Looking into distinct expression profiles between cell lines with high and low MYC expression, we revealed 201 non-canonical ORFs that are differentially expressed. We evaluate our data through various analyses including testing of which ORFs have a viable sequence context for translation, achieved through another in-house tool (TIS Transformer). CONCLUSIONS By taking advantage of advances in machine learning, we were able to uncover new sites of translation in medulloblastoma that can aid the future discovery of therapeutic targets. As such, we were able to find 22 highly promising non-canonical ORFs that show unique expression profiles in relation to MYC in medulloblastoma.

Accessing Ping Sonar Echosounder Produksi ROVMAKER pada Mikrokontroler dan Data Noise Reduction dengan Kalman Filter

Two-thirds of Indonesia's territory is waters that have a high risk of flooding. Bathymetric surveys are important for flood management because they provide depth and topography data to determine the need for dredging of a water area. Traditional bathymetric surveys produce less than optimal coverage due to limited ship movements. This research aims to develop an automatic bathymetry system using a ping sonar echosounder sensor integrated with a USV (Unmanned Surface Vehicle). The ping sonar echosounder produced by ROVMAKER can generally only be accessed using the built-in software from the ROVMAKER manufacturer and can now be integrated with the microcontroller system on the USV to increase the efficiency of bathymetric system performance. This research also uses the Kalman filter method to reduce sensor reading noise. Validation of the suitability of the sensor shows an average error of 2.94% at a depth of 1400mm and 2.195% at a depth of 2200mm, which shows that the sensor is classified as suitable for use. The optimal noise reduction in the Kalman filter experiment is at a variance ratio (R/Q) of 1000 with an RMSE of 5.05mm at a depth of 1400mm and 3.99mm at a depth of 2200mm. This system has been proven to increase the accuracy of bathymetric data and system access efficiency.

Stochasticity in ferroelectric memory devices with different bottom electrode crystallinity

This study comprehensively analyzes of the influence of bottom electrode crystallinity on the stochastic noise in ferroelectric tunnel junctions (FTJs). We perform a comparative analysis of stochastic read noise in FTJ devices with crystalline silicon (FTJc-Si) and poly-silicon (FTJpoly-Si) bottom electrodes by utilizing low-frequency noise spectroscopy and multi-frequency conductance measurements. The results indicate that FTJpoly-Si exhibits a 50 % higher read current fluctuation in the high-resistance state (HRS) at high read currents compared to FTJc-Si due to differences in interface trap density and the subsequent barrier height fluctuations. Although the crystallinity of the bottom electrode affects the noise in the HRS, the noise in the low-resistance state (LRS) remains consistent with the consequence of bulk-limited Poole-Frenkel emission. These findings clarify the relationship between bottom electrode crystallinity and stochastic noise in FTJs, providing crucial insights for the design and operation of future ferroelectric-based memory.

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights. Enhancing READ resolution is very important to improving the reliability of memristive synapses. Usually, the READ resolution is very small for a memristive synapse with a 4-bit data precision. This work considers a step-by-step analysis to enhance the READ current resolution or the read current difference between two resistance levels for a current-controlled memristor-based synapse. An empirical model is used to characterize the HfO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\hbox {HfO}}_{2}$$\\end{document} based memristive device. 1st\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\ extrm{st}$$\\end{document} and 2nd\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2\ extrm{nd}$$\\end{document} stage device of our proposed synapse design can be scaled to enhance the READ current margin up to ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 4.3×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} and ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 21%, respectively. Moreover, READ current resolution can be enhanced with run-time adaptation techniques such as READ voltage scaling and body biasing. The READ voltage scaling and body biasing can improve the READ current resolution by about 46% and 15%, respectively. TENNLab’s neuromorphic computing framework is leveraged to evaluate the effect of READ current resolution on classification, control, and reservoir computing applications. Higher READ current resolution shows better accuracy than lower resolution even when facing different levels of read noise.

Optimal Fitting and Debiasing for Detectors Read Out Up-the-Ramp

This paper derives the optimal fit to a pixel’s count rate in the case of an ideal detector read out nondestructively in the presence of both read and photon noise. The approach is general for any readout scheme, provides closed-form expressions for all quantities, and has a computational cost that is linear in the number of resultants (groups of reads). I also derive the bias of the fit from estimating the covariance matrix and show how to remove it to first order. The ramp-fitting algorithm I describe provides the χ 2 value of the fit of a line to the accumulated counts, which can be interpreted as a goodness-of-fit metric. I provide and describe a pure Python implementation of these algorithms that can process a 10-resultant ramp on a 4096 × 4096 detector in ≈8 s with bias removal on a single core of a 2020 Macbook Air. This Python implementation, together with tests and a tutorial notebook, are available at https://github.com/t-brandt/fitramp. A companion paper describes a jump detection algorithm based on hypothesis testing of ramp fits and demonstrates all algorithms on data from JWST.

Programming memristor arrays with arbitrarily high precision for analog computing.

In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches.

Negative capacitance FET based dual-split control 6T-SRAM cell design for energy efficient and robust computing-in memory architectures

A Negative Capacitance Field effet transistor (NCFET) based Dual split control (DSC) 6T-SRAM cell has been designed and explored with Computing-in memory (CiM) architecture for energy efficient demonstration of Deep neural networks (DNN) basic operation such as Input-Weight (Dot) Product. The impact of ferro electric layer thickness (Tfe) on the SRAM cell perfomance metrics such as read noise margin (RNM), write noise margin (WNM) and energy efficiency for read and write operations have been analyzed at supply voltages of 0.3 V and 0.5 V. It has been observed that due to the steep slope characteristics, the NCFET based DSC 6T-SRAM cell design exhibits better RM, WM, and energy efficiency as compared to the baseline CMOS DSC SRAM cell design at VDD = 0.3 V and 0.5 V respectively (with Tfe range of 1 nm to 3 nm). Further, NCFET dual split control scheme for 6T-SRAM cell demonstrate improved read stability and write ability when compared with NCFET 6 T-SRAM cell design along with improved energy efficiency. NCFET based DSC 6T-SRAM CiM cell design has ∼22.77× and 12.41× lower energy consumption compared to the équivalent baseline 40 nm CMOS/baseline SRAM CiM design and ∼ 25.80× and 22.76× lower energy consumption compared to the NCFET based SRAM CiM at VDD = 0.3 V and 0.5 V respectively. NCFETs have improved steep subthreshold slope characteristics at an optimal Tfe value and NCFET SRAM based CiM circuits are expected to have higher noise margins and lower energy consumption compared to the baseline CMOS designs and are effective for NCFET based computing in-memory architectures with reduced read disturb issues in combination with DSC concept.

Arrhythmia classification for non-experts using infinite impulse response (IIR)-filter-based machine learning and deep learning models of the electrocardiogram.

Arrhythmias are a leading cause of cardiovascular morbidity and mortality. Portable electrocardiogram (ECG) monitors have been used for decades to monitor patients with arrhythmias. These monitors provide real-time data on cardiac activity to identify irregular heartbeats. However, rhythm monitoring and wave detection, especially in the 12-lead ECG, make it difficult to interpret the ECG analysis by correlating it with the condition of the patient. Moreover, even experienced practitioners find ECG analysis challenging. All of this is due to the noise in ECG readings and the frequencies at which the noise occurs. The primary objective of this research is to remove noise and extract features from ECG signals using the proposed infinite impulse response (IIR) filter to improve ECG quality, which can be better understood by non-experts. For this purpose, this study used ECG signal data from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) database. This allows the acquired data to be easily evaluated using machine learning (ML) and deep learning (DL) models and classified as rhythms. To achieve accurate results, we applied hyperparameter (HP)-tuning for ML classifiers and fine-tuning (FT) for DL models. This study also examined the categorization of arrhythmias using different filters and the changes in accuracy. As a result, when all models were evaluated, DenseNet-121 without FT achieved 99% accuracy, while FT showed better results with 99.97% accuracy.

RoLoMa: robust loco-manipulation for quadruped robots with arms

Deployment of robotic systems in the real world requires a certain level of robustness in order to deal with uncertainty factors, such as mismatches in the dynamics model, noise in sensor readings, and communication delays. Some approaches tackle these issues reactively at the control stage. However, regardless of the controller, online motion execution can only be as robust as the system capabilities allow at any given state. This is why it is important to have good motion plans to begin with, where robustness is considered proactively. To this end, we propose a metric (derived from first principles) for representing robustness against external disturbances. We then use this metric within our trajectory optimization framework for solving complex loco-manipulation tasks. Through our experiments, we show that trajectories generated using our approach can resist a greater range of forces originating from any possible direction. By using our method, we can compute trajectories that solve tasks as effectively as before, with the added benefit of being able to counteract stronger disturbances in worst-case scenarios.

Multi-Axis Laser Interferometer Not Affected by Installation Errors Based on Nonlinear Computation

Most multi-axis laser interferometers require accurate installation of the lasers and detectors since the position and orientation installation errors of lasers and detectors bring measurement error to the interferometer. In this paper, the multi-axis interferometer based on nonlinear computation is proposed, which avoids the measurement error caused by installation errors by taking the laser position and orientation as unknowns into the measurement model and discussing the solution component uniqueness of a nonlinear equation system. The simulation results show that even when the installation errors are close to 1 μm and 1 μrad, the proposed interferometer can still measure the multi-degree-of-freedom displacement accurately, and the root mean standard error (RMSE) of displacement is 1.884 nm and 5.871 × 10−7 mrad under a reading noise level of 0.1 nm.

M-Denoiser: Unsupervised image denoising for real-world optical and electron microscopy data

Real-world microscopy data have a large amount of noise due to the limited light/electron that can be used to capture images. The noise of microscopy data is composed of signal-dependent shot noise and signal-independent read noise, and the Poisson–Gaussian noise model is usually used to describe the noise distribution. Meanwhile, the noise is spatially correlated because of the data acquisition process. Due to the lack of clean ground truth, unsupervised and self-supervised denoising algorithms in computer vision shed new light on tackling such tasks by utilizing paired noisy images or one single noisy image. However, they usually make the assumption that the noise is signal-independent or pixel-wise independent, which contradicts with the actual case. Hence, we propose M-Denoiser for denoising real-world microscopy data in an unsupervised manner. Firstly, the shatter module is used to break the dependency and correlation before denoising. Secondly, a novelly designed unsupervised training loss based on a pair of noisy images is proposed for real-world microscopy data. For evaluation, we train our model on optical and electron microscopy datasets. The experimental results show that M-Denoiser achieves the best performance both quantitatively and qualitatively compared with all the baselines.

Optimization of Projected Phase Change Memory for Analog In‐Memory Computing Inference (Adv. Electron. Mater. 6/2023)

Large Neural Networks In article number 2201190, Ning Li and co-workers systematically tune phase change memory electrical properties using a projection liner, which also reduces resistance drift and read noise. The inference accuracy of large deep neural networks is improved for both the short term and long term after programming. The device optimization space is identified to achieve the best accuracy for large neural networks with real applications.

Optimization of Projected Phase Change Memory for Analog In‐Memory Computing Inference

AbstractPhase change memory (PCM) is one of the most promising candidates for non‐von Neumann based analog in‐memory computing–particularly for inference of previously‐trained deep neural networks (DNN). It is shown that PCM electrical properties can be tuned systematically using a projection liner, which is designed for resistance drift mitigation, in the manufacturable mushroom PCM. A systematic study of the electrical properties‐including resistance values, memory window, resistance drift, read noise, and their impact on the accuracy of large neural networks of various types and with tens of millions of weights is performed. It is sown that the DNN accuracy can be improved by the PCM with liner for both the short term and long term after programming, due to reduced resistance drift and read noise, respectively, despite the trade‐off of reduced memory window. The liner conductance, PCM device characteristics, and network inference accuracy with PCM memory window and reset state conductance is correlated, which allows us to identify the device optimization space to achieve better short term and long term accuracy for large neural networks.

Statistics of electron-multiplying charge-coupled devices

Electron-multiplying charge-coupled devices are efficient imaging devices for low-surface-brightness ultraviolet astronomy from space. The large amplification allows photon counting (PC), the detection of events versus nonevents. This paper provides the statistics of the observation process, the photon-counting process, the amplification process, and the compression. The expression for the signal-to-noise of PC is written in terms of the polygamma function. The optimal exposure time is a function of the clock-induced charge. The exact distribution of amplification process is a simple-to-compute powered matrix. The optimal cutoff for comparing to the read noise is close to a strong function of the read noise and a weak function of the electron-multiplying gain and photon rate. A formula gives the expected compression rate.

High Restore Yield NVSRAM Structures With Dual Complementary RRAM Devices for High-Speed Applications

Static random access memory (SRAM) plays a key role in the overall performance of electronic systems because of its rapid data processing and transmission speed; however, when the system power supply is cut off, the data stored in the nodes are lost. Thus, this article proposes four nonvolatile SRAM (NVSRAM) cells that use unilateral or bilateral structures with dual complementary series resistive random access memory (RRAM) devices. It is found that the read, write, and hold static noise margins (HSNMs) are comparable with those of the standard 6T-SRAM. Moreover, the store and restore operations operate in parallel at high speed. The store operation delay is only 6 ns for unilateral structures and 5 ns for bilateral structures, and the restore delay is only 10 ns for unilateral structures and 6 ns for bilateral structures. The maximum power consumption among the four structures for storing and restoring a “1” are 1.545 pJ/bit and 134.5 fJ/bit, respectively. Furthermore, the dual complementary series resistor structures can achieve a high restore yield at a resistance ratio of 1.5. Therefore, a high restore yield can be achieved even with large resistance fluctuations caused by the voltage, time, and process.