The goal of this research is to develop a predictive model that determines how low-frequency Electromagnetic Interference (EMI) affects the leakage current behavior of CMOS transistors. Although developed and validated using NMOS devices, the modeling framework can be extended to PMOS transistors; experimental validation of PMOS devices is planned for future work. The model provides essential physical parameter-based analysis of nanoscale device EMI susceptibility during low-frequency operation. The model demonstrates high accuracy and practicality through experimental verification of test chips built with standard TSMC CMOS technology nodes. The findings highlight that modern CMOS designs must account for low-frequency EMI, which can induce leakage shifts significant enough to impact EMC compliance, functional robustness, and reliability in ultra-low-power and near-threshold applications. The research delivers a practical method for designers to evaluate and reduce EMI-induced leakage in integrated circuits.

Electromagnetic interference (EMI) has become a serious challenge for signal integrity (SI) in modern high-speed digital systems. With the technology scaling down into nanometric CMOS technologies and lowering supply voltage, EMI induced signal integrity effects are becoming more significant compared to voltage margins. This work presents experimental results of controlled RF interference affecting the eye diagrams of the CMOS inverter. The test circuits were fabricated in 65 nm, 130 nm, and 180 nm CMOS technologies. A dedicated measurement methodology has been developed to inject RF to the supply node and to capture both time domain and eye diagram signals to visualize EMI effects. Unlike previous works that analyzed effects of channel-induced impairments or presented simulation results on EMI effects, we present an experimental evaluation of the impact of EMI on circuit functionality. Experimental results reveal that EMI tends to modulate the logic-high level amplitude, causing progressive eye closure. Consistent with these results, the eye height is found to decrease monotonically with increasing RF power for all technology nodes, while the logic-low level is found to be less affected. Asymmetric CMOS inverter susceptibility is thus found to be technology independent and sensitive only to the conduction state of the inverter. To illustrate the degradation caused by EMI, a compact, analytical expression for the reduction of the eye height is derived. The reduction is given as a function of the RF interference amplitude and expressed through a technology-dependent scaling parameter. Good agreement is observed between simulated results and experiments for varying interference amplitudes and different technology generations. Results are presented to demonstrate eye height as a sensitive and reliable metric of EMI susceptibility. Additionally, a practical framework for rapid estimation of signal degradation is presented for high-speed digital systems operating in complex electromagnetic environments.

This research investigates Time-Reversal (TR) methods for detecting the resonances of passive microwave structures contained in a multipath environment. The multipath environment is realized using a reverberant, 3D, microwave cavity with irregularly-shaped metal boundary walls. The experimental setup utilized MATLAB automation to link an arbitrary waveform generator (AWG) with a real-time oscilloscope for repeated signal transmission and acquisition and TR operations inside the reverberant cavity. To demonstrate the technique, two C-band microstrip patch antennas with distinct resonant frequencies were designed, fabricated, and considered as the Device Under Test (DUT). The developed method shows that time-domain TR processing can recover the resonant response of passive structures, including both amplitude and phase information, through adaptive processing. To date, there has been no experimental validation of resonance detection utilizing time-domain TR within a deliberately wave-chaotic electromagnetic enclosure—where multipath propagation, modal overlap, and environmental loading predominantly influence the system response. The results highlight the potential of time-domain TR as a practical tool for radar sensing, passive resonance measurement, and EMI/EMC testing in complex electromagnetic environments.

Electromagnetic interference (EMI) can cause both amplitude noise and timing error in the information transmitted by electro-optic modulators (EOMs). A simple predictive model for conducted EMI-induced timing jitter in EOMs is presented in this article. The timing jitter observed in the receiver end eye diagram contributed by a low-frequency (${\sim }$15 MHz) RF signal interference in a Mach-Zehnder modulator (MZM) is investigated. The developed model successfully predicts the change in the induced periodic jitter as a function of the injected noise power in the MZM. The model is used to identify and compare the impact of amplitude and timing noise in the eye diagram due to RF interference in an MZM modulation system. A design space for the EOMs operating in an electromagnetic environment is also presented to demonstrate the utility of the developed predictive amplitude and timing noise models.

The coupling of electromagnetic interference (EMI) from extraneous Radio Frequency (RF) sources on digital electronic interconnects is of critical importance in the Electromagnetic Compatibility (EMC) engineering community. Typically, in such analysis, the EMI is treated as a Continuous Wave (CW) signal that impinges on the digital electronic system. However, it should be noted that EMI signals can have varied temporal and spectral characteristics, and the question arises on how to incorporate waveform modulation into EMI analysis of digital interconnects. In this paper, we present a mathematical formulation on how to incorporate waveform modulation into EMI coupling analysis and utilize it to demonstrate that specific waveform modulations can lead to enhanced coupling effectiveness as compared to conventional CW stimulus. In a test case studied in this paper, the use of our methodology suggests that an optimally modulated EMI signal can enhance coupling by 27% as compared to a single-tone CW EMI signal. We also present an analytical optimization method to obtain the optimal EMI waveform modulation that maximizes the coupling effectiveness of EMI using a Fourier series approximation approach. This technique can be utilized by EMC engineers to develop robust shielding margins for various classes of EMI signals.

We propose a novel data-driven approach for synthesizing unintended emission signals that can then be used to improve and optimize regulatory thresholds. The main component of our approach is a GAN's generator which is trained with a collection of unintended emission and then, in the inference phase, is asked to produce new and similar signals. We demonstrate that, with correlation as the measure of similarity, the GAN-generated signals are similar to the dataset.

Electro-optic modulators (EOMs) are an interface between electrical and optical components, making them susceptible to external electromagnetic interference (EMI). In this article, we investigate the effects of low-frequency ( ${\sim }$ 15 MHz) EMI on lithium niobate (LiNbO 3 ) EOMs. A predictive model for low-frequency EMI injection into a commercially available EOM is developed and validated experimentally. Specifically, analytical expressions for eye height and Q-factor are derived that predict the changes in the receiver end eye diagram, when the EOM modulation process is interrupted by the RF injection. The eye diagram gradually closes down as the RF injection power increases, owing to the signal degradation in the receiver end. A simple expression for calculating the maximum peak RF injected voltage that the EOM can tolerate prior to the system experiencing significant bit error rate is also presented. This model can be utilized to assess the survivability of EOMs under EMI.

The coupling of short wavelength electromagnetic (EM) interference to critical electronic systems in highly reverberant enclosures is a growing concern in the EM interference/compatibility community. In such highly reverberant cavities, prior research has shown that the induced EM fields, voltages, or currents can be modeled using the wave-chaotic random coupling model (RCM). The RCM partitions the interaction within the cavity into a universally fluctuating part, derived from random matrix theory, and a system-specific part, defined by the radiation impedance of the ports of interest, where the voltages or currents are induced. Earlier researchers have treated the radiation impedance as a time-invariant, frequency-dependent complex quantity. This is true for passive structures but is not true for active semiconductor devices, such as microcontrollers, which can exhibit time-varying radiation properties depending on the instruction cycle being executed at a given instant of time. The estimate of such a time-varying radiation impedance and its correlation with instruction cycles in an elemental microcontroller is the focus of this work. Utilizing clustering algorithms, we observe that the measured radiation impedance, as well as radiative emissions, are correlated to the class of instruction cycles being executed. By clustering the radiation impedance and emissions of the general-purpose input/output ports utilizing the class of instructions being executed, predictive models for microcontroller susceptibility can be derived even when detailed knowledge of the specific instruction cycle being executed is unknown a priori. Such a predictive capability can find multiple applications in the EMI/EMC community.

In this paper, a predictive model is developed to characterize the impact of high-frequency electromagnetic interference (EMI) on the leakage current of CMOS integrated circuits. It is shown that the frequency dependence can be easily described by a transfer function that depends only on a few dominant parasitic elements. The developed analytical model is successfully compared against measurement data from devices fabricated using 180 nm, 130 nm, and 65 nm standard CMOS processes through TSMC. Based on the predictive model, the impact of EMI on leakage current in a CMOS inverter is reduced by increasing the frequency from 10 MHz to 4 GHz.

This abstract covers Part-II of a two-part presentation series on the scientific advancements made in the AFOSR/AFRL Center of Excellence (CoE) for Electronics in Extreme Electromagnetic Environments, spanning the time-period 2015-present. In specific, this presentation focuses on the development and experimental validation of statistical and deterministic physics-based predictive models describing the functional state of electronic devices (semiconductor, electro-optic and quantum), and the amalgamation of these devices to circuits and subcomponents, when subjected to extreme electromagnetic interference (EEMI). This presentation follows a companion presentation [1] which discusses the development and experimental validation of statistical and deterministic physics-based models describing coupling paradigms for EEMI in complicated enclosures which houses these sensitive electronic devices, circuits and subcomponents. Taken together, the two presentations advance the state-of-the-art in fundamental physics-based modeling of current and future electronic technologies in extreme electromagnetic environments.

This AFOSR Center of Excellence established in 2015 has been studying the science of electronics in extreme electromagnetic environments. This presentation focuses on advancing computational electromagnetic techniques for statistical and deterministic extreme electromagnetic interference (EEMI) modeling; on EEMI interactions within wave chaotic enclosures; and with EEMI interactions in networked complex enclosures. A companion presentation discusses EEMI effects on elemental CMOS devices, electro-optic devices, mathematical frameworks for modeling EEMI effects on digital logic circuits, and modeling EEMI effects on software execution.

In this article, we focus on developing predictive upset models to characterize the nonpersistent effects of large-signal gate-side injection on n-type and p-type metal-oxide semiconductor field-effect transistors (mosfets). By “nonpersistent,” we refer to a set of conditions that do not affect the physical characteristics of the device or exhibit any memory effects, i.e., the device will operate normally once the injected large signal stimulus is removed. We present predictive models that determine the maximum limits for large-signal gate-side injection in terms of the device's ION/IOFF ratio prior to degradation or damage to the device. A function based on the mosfet technology device parameters, such as its threshold voltage and its power supply rating, is developed and presented. We then validate our predictive models against experimentally measured data for complementary metal-oxide-semiconductor (CMOS) mosfet devices fabricated using 350, 180, 130, and 65 nm standard Taiwan Semiconductor Manufacturing Company (TSMC) CMOS processes. Based on our validated predictive models, we show how the maximum limits for large-signal gate-side injection at 10-MHz change with technology scaling, from ~9.7 dBm for 350 nm to ~-1.7 dBm for 65-nm technology nodes for n-type mosfets; and, from ~11.0 dBm for 350 nm down to ~1.2 dBm for 65-nm technology nodes for p-type mosfets. We anticipate that our predictive models can be leveraged by CMOS circuit designers and electromagnetic interference/compatibility (EMI/EMC) engineers as quick “rule of thumb” guidelines to estimate device and circuit level susceptibility for the injected large signals.

Predicting the behavior of digital electronic and embedded systems subjected to extreme electromagnetic interference (EEMI) is a growing concern for military and civilian systems operating in ever denser ambient electromagnetic (EM) or Radio Frequency (RF) environments. While the interaction of the EM/RF stimulus with the system circuitry occurs at the physical or hardware level ("glitch"), the manifestation of this interaction is often observed as an erroneous behavior in the software state of the system under EM/RF stress. In this paper, we present ongoing progress in developing Markov State Vector Machine (MSVM) modeling frameworks for describing the software state of a microcontroller-based digital electronic system subjected to an externally applied glitch. We also describe our ongoing work in developing predictive MSVM models for the erroneous responses of complex software scripts when the underlying digital electronic system is subjected to EEMI glitches. Our predictive capability will help EMI/EMC engineers develop quick assessment tools for modeling the behavior of higher complexity digital electronic and embedded systems exposed to extreme electromagnetic environments.

In this paper, we present a theoretical framework for modeling the empirically observed cascading of software failures on a complicated computing system exposed to extreme electromagnetic interference (EEMI). Our approach is to treat the temporal evolution of the electromagnetic coupling and the resultant cascading series of electromagnetic-induced faults as the "flow" in a dynamic fluid-mechanical system and thereby utilize aspects of the Navier Stokes and Hamilton-Jacobi equations to predict the rate of this flow. Therefore, inspired by the concepts of fluid dynamics [1], we include a diffusion term in the Hamilton-Jacobi-Isaacs (HJI) equation. We have considered two approaches. In one we apply a Taylor expansion to the optimality principle and consider additional terms; in the other scenario, we simply add a diffusion term in the form of a Laplacian applied to the cost function H(x,...) and some constant c, as it is present in the Navier-Stokes equation for incompressible flow. We provide numerical comparisons for both approaches with respect to the original HJI equation where the dynamical vector field corresponds to analytical models of a NOR logic gate. This model is a second-order differential equation that describes the behavior of the gate that incorporates a new term accounting for EEMI injection.

We present a second order dynamical system to represent the behavior of a D flip-flop. We employ windowing functions and vector fields to replicate a characteristic found in [1],which resorts to switching. The model also takes into account metastable behavior, which can be exploited when studying software execution faults due to an undefined logical state. We conceived the noise injection to be additive noise targeting the transition between the stable equilibrium points. However, the model is flexible and many parameters can be changed to alter its behavior.

This paper presents an analytical model that predicts and characterizes the impact of Electromagnetic Interference (EMI) on the leakage current of CMOS integrated circuits. It is shown that the rate of increase in leakage current follows the modified Bessel function, which can be estimated using only a few primitive device parameters. Unlike other device and circuit parameters, leakage current is very sensitive to EMI, where only a few hundred millivolts of noise can increase the leakage current by a factor of 1000. The developed analytical model is successfully compared against measurement data from CMOS inverters fabricated using various TSMC's standard CMOS processes, including 180 nm, 130 nm, and 65 nm. Based on the predictive model and experimental data, we show that the impact of EMI on the leakage current of CMOS integrated circuits is independent of technology scaling.

Extreme Electromagnetic Interference (EEMI) can cause device malfunction due to reparable upsets before any permanent hardware damage occurs to electronic devices. In this paper, a predictive model is developed to characterize the impact of EEMI on Metal-Oxide Semiconductor Field-Effect Transistors (MOSFETs) prior to any such permanent damage. The predictive model determines the onset of tolerance limits of EEMI on the Ion/Ioff ratio of a MOSFET for a given technology node, using only the most fundamental device parameters - such as the threshold voltage and power supply. The developed model is successfully compared against measurement data from a device fabricated using 350nm standard CMOS process through TSMC. Based on the predictive model the tolerance of the EEMI injected power in a MOSFET reduces due to technology scaling, starting from 9.7dBm at 350nm, and down to -1.7dBm at 65nm technology node.

We describe the process of calculating reachable sets for the dynamics of a hybrid model of logic gates. We then use this framework to estimate the impact of electromagnetic interference on the execution of a segment of code. We consider temporal changes as well as changes to the initial conditions in the logic gates, and we consider their impact on the intended software execution.

This paper presents an analytical model to predict and characterize the impact of Extreme Electromagnetic Interference (EEMI) on Voltage Transfer Characteristic (VTC) of CMOS inverters as a function of device scale. The predictive model determines the slope of VTC based on only a few primitive technology parameters. The developed analytical model is successfully compared against measurement data from a CMOS inverter fabricated using TSMC's 350nm standard CMOS process. Based on the predictive model the tolerance to EEMI injected power in a CMOS inverter reduces by technology scaling, starting from 14dBm at 350nm down to 3.5dBm at 65nm technology node.

A simulation environment for studying the effects of electromagnetic perturbations on software execution is presented. We execute a program inside the processor and then we introduce glitches at different points in time and at different locations to modify the program execution. We manage to alter the state of a counter program by introducing glitches taking into account the target instruction and the timing diagram associated with it.