Spatial EMFI Mapping

• Spatial EMFI Mapping is a systematic approach that quantifies and visualizes electromagnetic susceptibility in microelectronic devices using controlled spatial probing.
• It integrates methodical grid scanning, timed EM pulse injection, and firmware instrumentation to produce detailed sensitivity maps and fault classifications.
• This technique aids in vulnerability assessment and robust design by identifying fault-prone regions across platforms such as microcontrollers and CPUs.

Spatial Electromagnetic Fault Injection (EMFI) Mapping is a systematic methodology for quantifying and visualizing the spatial sensitivity of microelectronic devices—most notably microcontrollers and CPUs—to EMFI attacks. It enables repeatable discovery of fault-prone regions by coupling spatial probe movements with controlled fault injection and fine-grained firmware-level observability, culminating in the construction of sensitivity maps and fault taxonomies. This approach underpins vulnerability assessment, fault model development, and the design of both attacks and robust defensive strategies.

1. Methodological Foundations

Spatial EMFI mapping employs an experimental workflow that integrates spatial probing, precisely timed EM pulse injection, firmware instrumentation for unambiguous fault detection, and automated behavioral classification. The methodology is platform-agnostic, relying on three sequential phases:

1. Spatial Probing and Parameter Exploration: A grid (uniform or adaptively refined) is defined over the target package; an EMFI probe is systematically positioned at each ($x, y$) coordinate, with controlled variations in pulse width, discharge voltage, probe height, and coil polarity. Injections are synchronized to deterministic program events (e.g., GPIO toggles or loop markers) to maximize temporal precision.
2. Architectural Instrumentation and Fault Observability: Firmware is instrumented to expose fault manifestations—either via explicit UART/GPIO markers (e.g., “CF_SKIP”, “CRC_ERR”), debug register snapshots, or memory checksums. This supports both real-time and post-mortem analysis of fault events.
3. Behavioral Fault Classification: Observed deviations are classified into high-level fault classes: control-flow faults, data-corruption faults, and system-level faults (resets, hangs, peripheral malfunctions). Assignment relies on simple firmware-encoded rules or threshold-based scripts, not statistical classifiers (Santhosh et al., 9 Dec 2025).

This process is highly automated and adaptable to a variety of platforms, including resource-constrained microcontrollers and large server-class CPUs (Santhosh et al., 9 Dec 2025, Kühnapfel et al., 2022).

2. Spatial Scanning Strategies and Sensitivity Metrics

A spatial EMFI scan involves methodical probe movement across a defined grid covering the chip surface:

• Grid Construction: The die surface is bounded by $x \in [x_{\mathrm{min}}, x_{\mathrm{max}}]$, $y \in [y_{\mathrm{min}}, y_{\mathrm{max}}]$, divided into $N_x \times N_y$ steps, with $$\Delta x = (x_{\mathrm{max}} - x_{\mathrm{min}})/(N_x - 1)$$ and analogously for $\Delta y$. Height $h$ and coil polarity $P$ are additional dimensions, enabling full 3D mapping or multiple layered 2D slices (Santhosh et al., 9 Dec 2025).
• Repetition per Coordinate: At each $(x_i, y_j)$, $T$ repeated injections are performed to ensure statistical reliability.

At each grid point, fundamental metrics are captured:

Metric	Formula	Interpretation
Fault Rate	$$f(x_i, y_j) = \frac{n_{\mathrm{faults}}(x_i, y_j)}{T}$$	Fraction of trials at $(x_i, y_j)$ yielding any fault
Bit-flip Probability	$$p_{\mathrm{flip}}(x_i, y_j) = \frac{n_{\mathrm{bits}}(x_i, y_j)}{B \cdot T}$$	Mean probability of bit error per trial on memory-centric targets

These scalar fields are visualized via 2D heatmaps or 3D scatter plots, directly revealing spatial sensitivity heterogeneity (e.g., CPU core hotspots or memory bands with elevated susceptibility).

3. Experimental Realizations and Platform Considerations

Practical EMFI mapping has been demonstrated on a range of devices, from commodity microcontrollers to x86 server CPUs:

• Microcontroller Platforms: On devices such as ESP32 (Xtensa LX6) and ChipWhisperer boards, EMMap employs a ChipSHOUTER® probe, fine-grained XY(Z) stages, and microsecond-level pulse alignment. Firmware-level observability leverages instrumented tight program loops, CRC-checksentinel memory, and debug port register dumps. Hotspot analysis reveals, for instance, that control-flow faults dominate regions near CPU-aligned probe positions, while data-faults localize to memory circuits (Santhosh et al., 9 Dec 2025).
• Large-scale CPUs: “EM-Fault It Yourself” describes a scalable mechanical/electronic/software architecture for high-resolution mapping of desktop/server-class SoCs, featuring micrometer-level probe movement, automated pulse triggering aligned to specific bus transactions or firmware events, and coordinate registration with respect to die photographs. Practical limitations, such as probe coil diameter (~4 mm) and stage accuracy (~2.5 µm), constrain the effective resolution and map fidelity (Kühnapfel et al., 2022).

Experimental scanning often adopts a coarse-to-fine grid strategy to balance coverage and throughput. High-susceptibility zones, once identified, receive denser re-scanning.

4. Fault Taxonomy and Scoring Functions

Every injection event is automatically assigned to one of three disjoint fault classes:

• Control-Flow Faults: Instruction skips, branch mispredictions, or loop escapes.
• Data-Corruption Faults: Bit-flips, multi-bit corruptions in memory or registers.
• System-Level Faults: System resets, hangs, peripheral failures.

For $(x, y)$, let $n_{cf}$, $n_{data}$, $n_{sys}$ be the number of respective class events. Define class ratios:

• $$R_{cf}(x, y) = n_{cf}(x, y)/n_{\mathrm{faults}}(x, y)$$,
• $$R_{data}(x, y) = n_{data}(x, y)/n_{\mathrm{faults}}(x, y)$$,
• $$R_{sys}(x, y) = n_{sys}(x, y)/n_{\mathrm{faults}}(x, y)$$.

Scoring functions, such as $S_{cf}(x, y) = R_{cf}(x, y)$, highlight regions with pronounced fault modality preference. Weighted sums allow adversary/defender-tailored region prioritization:

$$S_w(x, y)=w_{cf}R_{cf}(x, y)+w_{data}R_{data}(x, y)+w_{sys}R_{sys}(x, y)$$

Classification logic is embedded in firmware and log parsers (e.g., CRC mismatch ⇒ data-fault).

5. Best Practices, Reproducibility, and Platform Adaptation

Robust spatial EMFI campaigns adhere to several critical guidelines:

• Timing Alignment: Hardware-triggered EM pulses are aligned to deterministic firmware markers, with trigger delays swept in fine increments to capture the narrow vulnerability window.
• Firmware Instrumentation: Detectors employ CRC-guarded sentinels for memory faults, structured UART/GPIO event emission, and dense loop constructs for precise control-flow measurements.
• Parameter Logging and Reproducibility: Comprehensive recording of probe geometry, pulse parameters, coordinates, and firmware revisions underpins experiment reproducibility and cross-platform comparison.
• Adaptability: The methodology generalizes across architectures—native debug or I/O hooks swap in as needed; grid resolution adapts to packaging and stage precision; probe and pulse characteristics become tunable axes rather than fixed parameters (Santhosh et al., 9 Dec 2025).

Assumptions inherent to spatial EMFI mapping include positional limits imposed by stage mechanics (tens of microns for high-end stages), temporal resolution set by the trigger system (often limited to a few nanoseconds with advanced FPGA or oscilloscope-based timing), and mapping coordinates that typically reside in macroscopic package-space rather than die-level physical space unless delidding or CAD overlay is available.

6. Quantitative Results and Interpretation

Illustrative mapping experiments yield the following empirical insights:

• ESP32 (Xtensa LX6): Hotspot sensitivity peaks align with the CPU core area; $f(x, y)$ reaches maximal values near core-adjacent grid points, with control-flow faults dominating fault classification in these regions.
• ChipWhisperer SRAM Target: $p_{\mathrm{flip}}(x, y)$ registers a pronounced axis of data corruption directly beneath the probe centerline, confirming spatial localization dependent on probe orientation and coil geometry.
• STM32F3 (CW322): Mixed fault regions are observed, with $R_{cf}\approx0.6$ in segments that repeatedly bypass loop counting logic, corroborated by system-level failures (resets, hangs) at elevated pulse voltages (Santhosh et al., 9 Dec 2025).

On x86-class devices, spatial mapping reveals clusterings of specific fault payload responses (e.g., register corruption, firmware signature check bypass) with effective spatial resolution dictated by probe and mechanical parameters (Kühnapfel et al., 2022).

7. Limitations and Future Directions

Limitations of spatial EMFI mapping include sensitivity to environmental drift (temperature, supply voltage), the necessity for mechanical recalibration after probe exchanges, and the inability to resolve die topology beneath complex packaging without destructive analysis. Mapping resolution is upper-bounded by probe dimension and stage error. Temporal alignment improvements (e.g., FPGA-based delay lines) enhance mapping sharpness for time-critical fault events.

Emerging directions include integration of advanced spatial field reconstruction techniques—Gaussian Processes (Tesfay et al., 2022), deep generative priors (Mallik et al., 6 May 2024), and neural tangent kernels (Mallik et al., 7 Apr 2025)—to interpolate or predict EMFI sensitivity over unsampled points and to automate detection of anomalous fault behavior. Such methods, when underpinned by rigorous experimental spatial scans, offer the potential for automated vulnerability assessment and cross-architecture comparison at unprecedented scales.

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In summary, spatial EMFI mapping, as formalized in methodologies such as EMMap, transforms EMFI from an ad hoc attack technique into an empirical science characterized by rigorous spatial sampling, quantitative metrics, reproducible experiment design, and automated fault taxonomy (Santhosh et al., 9 Dec 2025, Kühnapfel et al., 2022). This ongoing progression enables nuanced understanding, systematic exploration, and evidence-backed mitigation of EMFI-induced vulnerabilities in contemporary microelectronic systems.