## Chart/Diagram Type: Multi-Panel Figure: Programming of ReRAM ### Overview The image presents a multi-panel figure (a-e) illustrating the programming of a CMO-HfOₓ ReRAM (Resistive Random-Access Memory) using a closed-loop scheme. The figure explores the relationship between target conductance, programming noise, and the number of iterations required for programming. ### Components/Axes **Panel a: Cumulative Distribution Function vs. Target Conductance** * **Title:** CMO-HfOₓ ReRAM during programming * **Axes:** * X-axis: Target Conductance [µS], ranging from 10 to 90 in increments of 10. * Y-axis: Cumulative Distribution Function, ranging from 0.00 to 1.00 in increments of 0.25. * **Color Scale:** A color gradient from blue to red, representing the number of states, ranging from 1 to 35. * **Annotation:** "Acceptance Range: 0.2% Gtarget" **Panel b: Identical-pulse closed-loop scheme** * **Title:** Identical-pulse closed-loop scheme * **Top Subplot Axes:** * Y-axis: V [a.u.] (Arbitrary Units) * Annotations: Vset (red), Vread (gray), Vreset (blue) * X-axis: time [a.u.] (Arbitrary Units) * **Bottom Subplot Axes:** * Y-axis: G [a.u.] (Arbitrary Units) * Annotation: Gtarget (green, dashed line) * X-axis: time [a.u.] (Arbitrary Units) * Annotation: ±Acc. Range (green bracket) **Panel c: Iterations vs Gtarget** * **Title:** Iterations vs Gtarget * **Axes:** * X-axis: Target Conductance [µS], ranging from 10 to 90 in increments of 20. * Y-axis: Closed-loop iterations (logarithmic scale), ranging from 10⁰ to 10². * **Data Series:** * Iterations (gray dots) * Avg per G (yellow dots) * Avg (dashed lines): * Acceptance Range 0.2% (purple, average around 89 iterations) * Acceptance Range 2% (yellow, average around 11 iterations) * **Annotations:** "Acc. Range 0.2%", "Acc. Range 2%" **Panel d: Prog. noise vs iterations** * **Title:** Prog. noise vs iterations * **Axes:** * X-axis: Closed-loop iterations (qualitative, Low to High) * Y-axis: σprog (qualitative, Low to High) * **Color Scale:** A color gradient from purple to yellow, representing the Acceptance Range, ranging from 0.2% to 2%. * **Annotations:** * "Trade-off" (white arrow) * "σprog ∈ [0.1, 1] µS, Iterations ≈ 10" (white cross at top-left) * "σprog ∈ [0.01, 0.1] µS, Iterations ≈ 90" (white cross at bottom-right) **Panel e: Prog. noise vs Gtarget** * **Title:** Prog. noise vs Gtarget * **Axes:** * X-axis: Target Conductance [µS], ranging from 10 to 90 in increments of 20. * Y-axis: σprog [µS] (logarithmic scale), ranging from 10⁻² to 10⁰. * **Data Series:** * Acceptance Range 0.2% (purple dots): σprog = 10⁻³ * (1.1 * G + 0.8) * Acceptance Range 2% (yellow dots): σprog = 10⁻³ * (11.3 * G + 11.2) * **Annotations:** "Acc. Range 0.2%", "Acc. Range 2%" ### Detailed Analysis **Panel a:** The cumulative distribution function shows how the states are distributed across different target conductance values. The color gradient indicates the density of states, with blue representing lower states and red representing higher states. The distribution shifts towards higher conductance values as the target conductance increases. **Panel b:** This panel illustrates the identical-pulse closed-loop scheme. The top subplot shows the voltage pulses applied (Vset, Vread, Vreset) over time. The bottom subplot shows the resulting conductance (G) over time, converging towards the target conductance (Gtarget) within an acceptable range (±Acc. Range). **Panel c:** This graph shows the relationship between the number of closed-loop iterations and the target conductance. The gray dots represent individual iterations, while the yellow dots represent the average number of iterations for each target conductance. The dashed lines indicate the average number of iterations for acceptance ranges of 0.2% (purple, ~89 iterations) and 2% (yellow, ~11 iterations). **Panel d:** This heatmap illustrates the trade-off between programming noise (σprog) and the number of closed-loop iterations. Lower noise requires more iterations, and vice versa. The color gradient represents the acceptance range, with purple indicating a tighter acceptance range (0.2%) and yellow indicating a wider acceptance range (2%). **Panel e:** This graph shows the relationship between programming noise (σprog) and target conductance. The purple dots represent an acceptance range of 0.2%, and the yellow dots represent an acceptance range of 2%. The equations provided describe the relationship between σprog and G for each acceptance range. ### Key Observations * **Panel a:** The distribution of states shifts towards higher conductance values as the target conductance increases. * **Panel b:** The closed-loop scheme converges towards the target conductance over time. * **Panel c:** A tighter acceptance range (0.2%) requires significantly more iterations than a wider acceptance range (2%). * **Panel d:** There is a clear trade-off between programming noise and the number of iterations. * **Panel e:** Programming noise increases with target conductance for both acceptance ranges. ### Interpretation The data presented in this figure demonstrates the programming characteristics of a CMO-HfOₓ ReRAM using a closed-loop scheme. The key findings are: 1. **Trade-off between Accuracy and Speed:** A tighter acceptance range (higher accuracy) requires more programming iterations (slower programming). 2. **Programming Noise Increases with Target Conductance:** As the target conductance increases, the programming noise also increases, making it more challenging to achieve precise programming at higher conductance levels. 3. **Closed-Loop Scheme Effectiveness:** The closed-loop scheme effectively converges towards the target conductance, but the number of iterations required depends on the desired accuracy and the target conductance value. The figure highlights the importance of carefully considering the trade-offs between accuracy, speed, and noise when programming ReRAM devices. The data can be used to optimize programming algorithms and device parameters to achieve the desired performance characteristics.

## Chart Compilation: CMOx-HfOx ReRAM Programming Characteristics ### Overview This image presents a compilation of charts illustrating the programming characteristics of CMOx-HfOx ReRAM devices. It explores the cumulative distribution function during programming, a closed-loop scheme for voltage application, the relationship between iterations and target conductance, and the impact of programming noise on iterations and target conductance. ### Components/Axes * **a) CMOx-HfOx ReRAM during programming:** * X-axis: Target Conductance [µS] (Scale: 0 to 90, with markers at 10, 20, 30, 40, 50, 60, 70, 80, 90) * Y-axis: Cumulative Distribution Function (Scale: 0.00 to 1.00, with markers at 0.00, 0.25, 0.50, 0.75, 1.00) * Color Scale: 1 to 35 (representing some property, likely related to the density of successful programming events) * Label: "Acceptance Range: 0.2% Gtarget" * **b) Identical-pulse closed-loop scheme:** * X-axis: time [a.u.] (arbitrary units) * Y-axis: V [a.u.] (arbitrary units) and States [a.u.] * Labels: Vset, Vread, Vreset, Gtarget, ±Acc. Range * **c) Iterations vs Gtarget:** * X-axis: Target Conductance [µS] (Scale: 10 to 90, with markers at 10, 30, 50, 70, 90) * Y-axis: Closed-loop iterations (Logarithmic scale, from 10^1 to 10^3) * Labels: "Acc. Range 0.2%", "Acc. Range 2%" * Data Series: Iterations (green circles), Avg per G (orange line), -Avg per G (blue line) * **d) Prog. noise vs iterations:** * X-axis: Closed-loop iterations (Scale: Low to High) * Y-axis: σprog (programming noise) (Scale: 0.2 to 2%) * Labels: σprog ∈ [0.1, 1]µs, Iterations = 10 (red X), σprog ∈ [0.01, 0.1]µs, Iterations = 90 (blue X), "Trade-off" * **e) Prog. noise vs Gtarget:** * X-axis: Target Conductance [µS] (Scale: 10 to 90, with markers at 10, 30, 50, 70, 90) * Y-axis: σprog, Range [µS] (Logarithmic scale, from 10^-2 to 10^0) * Labels: "Acc. Range 0.2%", "Acc. Range 2%" * Equations: σprog = 10^-3 * (11.3 * G + 11.2), σprog = 10^-3 * (1.1 * G + 0.8) ### Detailed Analysis or Content Details * **a) CMOx-HfOx ReRAM during programming:** The chart shows a cumulative distribution function. The color intensity indicates the density of data points. The "Acceptance Range" of 0.2% Gtarget is highlighted. The data suggests that achieving a specific target conductance has a distribution of outcomes, and the color gradient shows how this distribution changes as the target conductance increases. The density of points is highest around 30-50 µS. * **b) Identical-pulse closed-loop scheme:** This diagram illustrates a feedback control scheme. Vset, Vread, and Vreset are voltage pulses applied to the ReRAM device. The device state (conductance) is monitored, and the voltage is adjusted to reach the target conductance (Gtarget) within the acceptable range (±Acc. Range). * **c) Iterations vs Gtarget:** The green circles representing "Iterations" show a generally increasing trend, leveling off at higher target conductances. The orange line ("Avg per G") shows a decreasing trend, while the blue line ("-Avg per G") is relatively flat. The "Acc. Range 0.2%" and "Acc. Range 2%" regions are highlighted in blue and light blue, respectively. At a target conductance of 50 µS, the number of iterations is approximately 300. * **d) Prog. noise vs iterations:** This chart shows a trade-off between programming noise (σprog) and the number of iterations. Higher iterations (90) correspond to lower programming noise (around 0.2%), while lower iterations (10) correspond to higher programming noise (around 1.5%). * **e) Prog. noise vs Gtarget:** The chart shows that programming noise decreases with increasing target conductance. Two equations are provided to model this relationship. At a target conductance of 10 µS, the programming noise is approximately 0.011 µS, and at 90 µS, it is approximately 0.091 µS. ### Key Observations * The cumulative distribution function (a) indicates that achieving precise target conductance is challenging, with a spread of outcomes. * The closed-loop scheme (b) is designed to mitigate this challenge by dynamically adjusting the voltage based on the device's state. * There is a trade-off between the number of iterations and programming noise (d). * Programming noise decreases with increasing target conductance (e). * The number of iterations required to reach the target conductance increases with the target conductance, but plateaus at higher values (c). ### Interpretation The data suggests that programming CMOx-HfOx ReRAM devices requires careful control of the voltage pulses and iterations to achieve the desired conductance with acceptable precision. The closed-loop scheme is a promising approach to address the inherent variability in the programming process. The trade-off between iterations and programming noise highlights the need for optimization. The decreasing programming noise with increasing target conductance may be related to the physics of the ReRAM device, such as the formation and rupture of conductive filaments. The equations provided in (e) offer a quantitative model for this relationship. The acceptance range of 0.2% Gtarget is a critical parameter for reliable device operation, and the charts demonstrate how different factors influence the ability to meet this requirement. The data presented provides valuable insights for designing and optimizing ReRAM-based memory devices.

## [Multi-Panel Technical Figure]: CMO-HfOx ReRAM Programming Analysis ### Overview This composite figure presents a technical analysis of programming a CMO-HfOx Resistive Random-Access Memory (ReRAM) device using a closed-loop, identical-pulse scheme. It includes a conductance state distribution map, a schematic of the programming protocol, and three plots analyzing the relationship between programming iterations, target conductance, and programming noise (σ_prog) under different acceptance ranges. ### Components/Axes The figure is divided into five panels labeled **a** through **e**. **Panel a: CMO-HfOx ReRAM during programming** * **Type:** 2D Heatmap / Scatter Plot. * **X-axis:** "Target Conductance [µS]". Linear scale from 10 to 90 µS, with major ticks every 10 µS. * **Y-axis:** "Cumulative Distribution Function". Linear scale from 0.00 to 1.00, with major ticks every 0.25. * **Color Bar (Right):** Labeled "States". A vertical gradient from blue (bottom, value 1) to red (top, value 35). Major ticks at 1, 8, 16, 32, 35. * **Legend/Annotation:** A text box in the upper portion reads: "Acceptance Range: 0.2% G_target". * **Data Representation:** A grid of colored dots. Each column corresponds to a specific target conductance (G_target). The color of the dots in a column indicates the number of discrete conductance states (from 1 to 35) achieved for that target. The vertical spread (CDF) shows the distribution of programmed states. **Panel b: Identical-pulse closed-loop scheme** * **Type:** Two aligned schematic diagrams. * **Top Graph (Voltage vs. Time):** * **Y-axis:** "V [a.u.]" (arbitrary units). Labels: "V_set" (positive red pulses), "V_read" (smaller grey pulses), "V_reset" (negative blue pulses). * **X-axis:** "time [a.u.]". * **Flow:** Shows a sequence: a V_set pulse, followed by a V_read pulse, repeated multiple times (indicated by "..."), then a V_reset pulse, followed by a V_read pulse, and so on. * **Bottom Graph (Conductance vs. Time):** * **Y-axis:** "G [a.u.]". A horizontal green dashed line is labeled "G_target". Two horizontal black dotted lines above and below it define the "±Acc. Range". * **X-axis:** "time [a.u.]". Aligned with the top graph. * **Data Representation:** Circles represent measured conductance after each pulse. The circles change color from blue to green as they approach G_target. The final circle is solid green, within the acceptance range. **Panel c: Iterations vs G_target** * **Type:** Scatter Plot with overlaid trend lines. * **X-axis:** "Target Conductance [µS]". Linear scale from 10 to 90 µS. * **Y-axis:** "Closed-loop iterations". Logarithmic scale from 10^0 (1) to 10^2 (100). * **Legend (Top):** Two colored boxes: "Acc. Range 0.2%" (purple) and "Acc. Range 2%" (yellow-green). * **Legend (Bottom):** "Iterations" (small dots), "Avg per G" (solid line with circle markers), "---Avg" (dashed line). * **Data Series:** 1. **Purple dots/line (0.2% range):** Data points are scattered between ~10 and ~100 iterations. The average line ("Avg per G") fluctuates around 80-90 iterations. A horizontal dashed line labeled "89" indicates the overall average. 2. **Yellow-green dots/line (2% range):** Data points are scattered between ~1 and ~20 iterations. The average line fluctuates around 10-15 iterations. A horizontal dashed line labeled "11" indicates the overall average. **Panel d: Prog. noise vs iterations** * **Type:** 2D Heatmap / Contour Plot. * **X-axis:** "Closed-loop iterations". Labeled "Low" to "High". * **Y-axis:** "σ_prog" (Programming noise). Labeled "Low" to "High". * **Color Bar (Right):** Labeled "Acc. Range". Scale from 0.2 (blue) to 2% (yellow). * **Annotations:** * A white double-headed arrow labeled "Trade-off" runs diagonally from the bottom-left (low iterations, low noise) to the top-right (high iterations, high noise). * **Top-left (High noise, Low iterations):** A white 'X' and text box: "σ_prog ∈ [0.1, 1]µS, Iterations ≈ 10". * **Bottom-right (Low noise, High iterations):** A white 'X' and text box: "σ_prog ∈ [0.01, 0.1]µS, Iterations ≈ 90". **Panel e: Prog. noise vs G_target** * **Type:** Scatter Plot with fitted power-law curves. * **X-axis:** "Target Conductance [µS]". Linear scale from 10 to 90 µS. * **Y-axis:** "σ_prog [µS]". Logarithmic scale from 10^-2 (0.01) to 10^0 (1). * **Legend (Top):** Same as Panel c: "Acc. Range 0.2%" (purple) and "Acc. Range 2%" (yellow-green). * **Data Series & Equations:** 1. **Purple dots (0.2% range):** Data points follow an upward trend. A fitted curve is annotated with the equation: `σ_prog = 10^-3 * (1.1 * G + 0.8)`. 2. **Yellow-green dots (2% range):** Data points follow a steeper upward trend. A fitted curve is annotated with the equation: `σ_prog = 10^-3 * (11.3 * G + 11.2)`. ### Detailed Analysis * **Panel a:** Shows that for a given target conductance (G_target), the programming process can settle into one of many discrete states (1-35). The color gradient indicates that higher target conductances (right side, ~70-90 µS) are associated with a higher number of possible states (red, ~32-35), while lower targets (left side, ~10-30 µS) are associated with fewer states (blue, ~1-16). The CDF shows the probability distribution across these states for each G_target. * **Panel b:** Illustrates the feedback control loop. A set pulse (V_set) is applied, the conductance is read (V_read), and this is repeated until the measured conductance (G) falls within the predefined acceptance range (±Acc. Range) around the target (G_target). Reset pulses (V_reset) are interspersed, likely to prevent saturation or for device conditioning. * **Panel c:** Demonstrates a clear trade-off. Achieving a tighter acceptance range (0.2%) requires significantly more programming iterations (average ~89) compared to a looser range (2%, average ~11). The number of iterations is relatively constant across the range of target conductances (10-90 µS) for a given acceptance range, though with considerable scatter. * **Panel d:** Conceptualizes the fundamental trade-off between programming precision (low σ_prog) and speed (low iterations). High precision (low noise) demands many iterations, while fast programming (few iterations) results in higher noise. The color indicates that the 0.2% acceptance range (blue) corresponds to the high-iteration, low-noise regime, while the 2% range (yellow) corresponds to the low-iteration, high-noise regime. * **Panel e:** Quantifies the relationship between programming noise and target conductance. For both acceptance ranges, σ_prog increases linearly with G_target (on a log-linear plot, indicating a power-law relationship). The slope is much steeper for the 2% acceptance range (coefficient 11.3) than for the 0.2% range (coefficient 1.1), meaning noise worsens more rapidly with increasing conductance when the precision requirement is relaxed. ### Key Observations 1. **Discrete States:** The ReRAM device programs into a finite number of discrete conductance states, not a continuous range. 2. **Precision-Speed Trade-off:** There is an inverse relationship between programming precision (acceptance range/noise) and programming speed (iterations). This is explicitly highlighted in Panel d. 3. **Noise Scaling:** Programming noise (σ_prog) scales linearly with the target conductance value. The scaling factor is heavily dependent on the required precision (acceptance range). 4. **Iteration Consistency:** The number of iterations required to reach a target is largely independent of the target conductance value itself for a fixed acceptance range (Panel c), but the resulting noise is not (Panel e). ### Interpretation This figure characterizes the performance and fundamental limits of a specific closed-loop programming algorithm for CMO-HfOx ReRAM. The data suggests that: * **For applications requiring high precision (e.g., analog computing weights):** One must use a tight acceptance range (0.2%), which will result in slower programming (~89 iterations) but lower absolute noise levels, especially at lower conductance values. * **For applications prioritizing speed (e.g., digital memory):** A looser acceptance range (2%) can be used, enabling much faster programming (~11 iterations), but at the cost of higher conductance variability (noise), which scales poorly with increasing conductance. * **Device Physics Insight:** The linear scaling of σ_prog with G_target (Panel e) may reflect underlying physical mechanisms in the ReRAM filament formation/modulation, where achieving higher conductance states inherently involves more variability. The fitted equations provide a predictive model for noise based on target conductance and desired precision. * **System Design Implication:** The clear trade-off (Panel d) presents a design knob for system architects. They can choose an operating point on the precision-speed curve based on the specific requirements of the neural network or computing task being implemented on the ReRAM array. The data in Panels c and e provide the quantitative basis for making that choice.

## Heatmap: CMO-HfOₓ ReRAM during programming ### Overview A heatmap visualizing the cumulative distribution function (CDF) of target conductance values during ReRAM programming. The color gradient represents the density of data points, with blue indicating lower values and red higher values. ### Components/Axes - **X-axis**: Target Conductance [μS] (10–90 μS) - **Y-axis**: Cumulative Distribution Function (0.00–1.00) - **Color Scale**: Blue (low density) to Red (high density) - **Legend**: "Acceptance Range: 0.2% G_target" (annotated in the top-left region) ### Detailed Analysis - The heatmap shows a gradient from blue (left) to red (right), indicating increasing conductance values. - The acceptance range (0.2% G_target) is highlighted in the upper-left quadrant, where data points cluster densely. - No explicit numerical values are provided, but the color intensity suggests higher conductance values (e.g., >70 μS) dominate the red regions. ### Key Observations - Conductance values below 30 μS are underrepresented (blue regions). - The acceptance range annotation implies a focus on conductance values near the target threshold. ### Interpretation The heatmap demonstrates the distribution of target conductance during ReRAM programming, with a concentration of data points near the 0.2% acceptance range. This suggests variability in programming outcomes, particularly at lower conductance values. --- ## Diagram: Identical-pulse closed-loop scheme ### Overview A schematic of a closed-loop programming process for ReRAM, showing voltage pulses and conductance measurements over time. ### Components/Axes - **X-axis**: Time [a.u.] (sequential steps) - **Y-axis**: Voltage States (V_set, V_read, V_reset) and Conductance (G) - **Key Elements**: - Red bars: V_set pulses - Gray bars: V_read measurements - Blue bars: V_reset pulses - Green dashed line: G_target threshold - Acceptance range: ±0.2% G_target (annotated with arrows) ### Detailed Analysis - The process alternates between V_set (programming), V_read (measurement), and V_reset (reset). - Conductance (G) is measured after each V_read, with deviations from G_target marked by the acceptance range. - The green dashed line (G_target) acts as a reference for successful programming. ### Key Observations - Conductance measurements (G) are tightly controlled within ±0.2% of G_target. - V_set pulses are consistently applied to adjust conductance toward G_target. ### Interpretation This closed-loop scheme ensures precise control of ReRAM conductance by iteratively adjusting voltage pulses and measuring deviations from the target. The acceptance range indicates tolerance for minor variations. --- ## Scatter Plot: Iterations vs G_target ### Overview A log-scale plot comparing closed-loop iterations to target conductance (G_target) for two acceptance ranges (0.2% and 2%). ### Components/Axes - **X-axis**: Target Conductance [μS] (10–90 μS) - **Y-axis**: Closed-loop Iterations (10⁰–10²) - **Legend**: - Purple: Acceptance Range 0.2% - Green: Acceptance Range 2% - Black dashed line: Average iterations per G_target ### Detailed Analysis - **0.2% Acceptance Range (Purple)**: - Iterations increase with G_target, peaking at ~100 iterations for G_target ≈ 80 μS. - Data points cluster densely between 10–50 μS. - **2% Acceptance Range (Green)**: - Fewer iterations required (1–10 iterations) across all G_target values. - Data points are sparser but show a general upward trend. ### Key Observations - Tighter acceptance ranges (0.2%) require significantly more iterations, especially at higher G_target values. - The average iterations per G_target (black dashed line) suggests a logarithmic relationship. ### Interpretation Higher precision (0.2% acceptance) demands more iterations, highlighting the trade-off between accuracy and computational effort in ReRAM programming. --- ## Heatmap: Prog. noise vs iterations ### Overview A heatmap showing the relationship between programming noise (σ_prog) and closed-loop iterations, with color gradients indicating acceptance ranges. ### Components/Axes - **X-axis**: Closed-loop Iterations (Low to High) - **Y-axis**: Programming Noise (σ_prog) [μS] - **Color Scale**: Blue (low σ_prog) to Yellow (high σ_prog) - **Annotations**: - "Trade-off: σ_prog ∈ [0.01, 0.1] μS, Iterations ≈ 90" - "σ_prog ∈ [0.1, 1] μS, Iterations ≈ 10" ### Detailed Analysis - Lower σ_prog (blue regions) correlates with higher iterations (right side of the plot). - Higher σ_prog (yellow regions) aligns with fewer iterations. - The "trade-off" annotation emphasizes the inverse relationship between noise and iterations. ### Key Observations - σ_prog values below 0.1 μS require ~90 iterations, while values above 0.1 μS drop to ~10 iterations. - The acceptance range is not explicitly labeled but inferred from color intensity. ### Interpretation Reducing programming noise improves precision but increases the number of iterations needed, reflecting a critical balance in ReRAM optimization. --- ## Scatter Plot: Prog. noise vs G_target ### Overview A log-scale plot of programming noise (σ_prog) against target conductance (G_target), with two acceptance ranges (0.2% and 2%). ### Components/Axes - **X-axis**: Target Conductance [μS] (10–90 μS) - **Y-axis**: Programming Noise (σ_prog) [μS] (10⁻²–10⁰) - **Legend**: - Purple: Acceptance Range 0.2% - Green: Acceptance Range 2% - **Equations**: - σ_prog = 10⁻³*(11.3*G + 11.2) (0.2% range) - σ_prog = 10⁻³*(1.1*G + 0.8) (2% range) ### Detailed Analysis - **0.2% Acceptance Range (Purple)**: - σ_prog increases linearly with G_target (slope ≈ 11.3*10⁻³). - Data points form a tight curve from ~0.1 μS (G=10 μS) to ~1.2 μS (G=90 μS). - **2% Acceptance Range (Green)**: - σ_prog increases more slowly (slope ≈ 1.1*10⁻³). - Data points range from ~0.08 μS (G=10 μS) to ~1.0 μS (G=90 μS). ### Key Observations - Tighter acceptance ranges (0.2%) exhibit higher σ_prog, especially at higher G_target values. - The 2% range shows a flatter trend, indicating lower noise tolerance. ### Interpretation Programming noise scales with target conductance, with stricter acceptance ranges amplifying noise effects. This underscores the challenge of maintaining precision in high-conductance ReRAM devices.