The high development rate of wireless communication technologies (5G, 6G, in both systems, as well as increasing the number of IoT-based architecture) this has imposed strict requirements on the performance and reliability of RF circuits. The high-frequency signal processing circuits that make up the backbone of many systems are subjected to fluctuating thermal conditions more and more due to both dense integration and compact form factor combined with high power modes of operation. Some of the primary performance-related parameters that can be affected by temperature changes in device parameters mobility, threshold voltage and saturation current include: Gain, noise figure (NF), the linearity and power efficiency. In fact, most RF design flows are still based on simulations in steady or nominal temperature, and this cannot actually represent the real behavior during stressful conditions of thermo stress.

Although a few papers have tackled the issue of temperature impacts on analog and mixed-signal circuits, their methods generally depend on either the use of standalone thermo- or the use of the so-called static corner analysis. Such methods fail to provide real-time interaction of electrical performance with thermal characteristic. Moreover, compared to electrical and thermal analysis, there is a lack of tools that combine these inputs to form a dynamical, automated process. The literature displays the lack of methodologies that allow closed-loop co-simulation between a high-precision electrical simulator and a flexible computational environment that could process parametric sweeps, extraction of data and visualization in real-time. This inability of the integration restricts the early stage designers to early perform thermally conscious optimizations.

The background to this study is the predicament that requires an effective, flexible simulation platform that is not only capable of simulating the temperature-dependent behavior of the circuits but can maintain the automatic and dynamic simulation of the circuit in a broad spectrum of thermo-conditions. MATLAB has a strong data processing and scripting environment then HSPICE has sufficient transistor-level modeling so it is ideal to work with simulation flow within MATLAB. This close integration between the two environments would facilitate the creation of a strong co-simulation environment, which allows one to systematically investigate the impact of temperature on the degraded performance on RF circuits.

Within this presentation, we proposed an HSPICEco-simulation framework-integrated MATLAB design of thermal-aware modeling and analysis of RF circuitry in particular on RF thermal-awareness thermodynamic modeling and analysis through co-simulation framework. The structure is capable of automating the entire simulation procedure of temperature sweeps, moreover, it constantly keeps the circuit netlists and extracts performance indices such as gains, NF and return loss at every temperature point. The target circuit to be validated is a CMOS based 2.4 GHz Low Noise Amplifier (LNA). The results of simulation do reveal serious degradation of gain and noise figure with temperature increase which confirms the necessity of thermal-aware design practices. The framework provides an effective means to treat thermal effects as one that can be put on a scaling basis into early-stage RF circuit design and optimization.

RF performance exhaustively depends on carrier mobility, threshold voltage and saturation current whose changes rapidly depend on temperature variation of the RF circuit. Earlier work has been done on temperature-aware circuit simulation on standard SPICE simulators or on discrete thermal solvers in isolation. As an example, Jain at al.[1] presented the study on the effect of thermal noise on the performance of CMOS RF amplifiers, however, their simulations were performed with no automation nor real-time data processing. On the same note, Nguyen et al.[3] modeled the effect of self-heating of the RF CMOS circuits by use of 3D simulation tools like COMSOL. Such finite element solvers, which are accurate, are computationally demanding, however, and cannot be readily amenable to fast, iterative circuit-level simulation.

The lower-frequency domains have been able to explore co simulation environments that integrate electrical and thermal domains. Li and Wang[2] suggested MATLAB-SPICE co-simulation of analog circuits, and this was not demonstrated to the high-frequency RF case. Besides, nothing was done with regards to real-time simulation control, netlist reconfiguration and processing a batch of temperature points. Banu and Agrawal[4] relied on MATLAB as a tool of mm-wave transceiver blocks analysis, although, their research concentrated mostly on graphical representation of data and post-processing data analysis instead of automatizing the closed loop simulation.

Even more recent, thermal-aware and fault-tolerant hardware simulation development has evoked an increased interest in the robust simulation environment. The necessity of using and minimizing faults on reconfigurable hardware under environmental stress is also important as outlined by Tamm et al.,[5] which indirectly argues the case of environmentally-aware design practices. In an analogous manner, machine learning approaches to signal processing was also reviewed by Barhoumi et al.[6] in the application of signal processing towards adaptive tuning and anomaly detection, and this could in turn be applied to the thermally adaptive RF through a synergistic collaborative center of gravity as a future work direction.

Within the field of embedded and mission-critical system applications, Thompson and Sonntag[7] have investigated cyber-physical systems in smart hospitals, which also highlights the importance of resilient and reliable RF designs with respect to the healthcare context. Rucker et al.[8] also investigated AI applications of biomedical signal processing, where temperature-restorative RF circuits are more critical in the real-time data acquisition. Further, the network vulnerabilities with respect to routing attacks in VANETs were addressed by RANGISETTI and ANNAPURNA,[9] wherein communication security and timing could be compromised because of the failure of RF transceivers as a result of thermal drift.

In spite of these helpful pieces on contributions, a thick gap exists in the literature in terms of an integrated, thermal-aware simulation platform that specifically focuses on RF front-end circuits when it comes to co-simulation with respect to HSPICE/MATLAB. Most previous investigations cover either self-contained simulation tools or cannot automate thermal sweeps, performance monitoring and installed feedback that targets updating the parameters of temperature. This paper meets this demand by proposing a script-driven framework that is scalable and allows temperature dependent modeling in real time, automated performance analysis, and visualization of RF circuits like CMOS based Low Noise Amplifiers (LNAs).

The following section explains the architecture, building blocks, and functional flow of the proposed co-simulation framework that was developed to assess the performance of RF circuit in regard to thermal variation. The framework is implemented to combine the accuracy of a transistor level HSPICE simulations and the scripting versatility and data analytics of Matlab so that automated and temperature-conscious simulations and result processing can be performed.

The simulated results by the proposed HSPICEMATLAB co-simulation showed the effectiveness of thermal variations on RF performance of the CMOS LNA. The parameters of gain, noise figure (NF), input return loss (S 11 ), and power use were measured as key performance parameters at three representative characteristics of gain figure (NF), input return loss (S 11 ), and power use were measuredtemperature points: 25 °C (room temperature), 75 °C (moderate thermal stress), and 125 °C (extreme high-temperature scenario).

The LNA has a significant gain deterioration as temperature increases as indicated in Table 1. The main cause of this degradation is the loss of carrier mobility and consequent loss of transconductance of the MOSFETs which has a direct effect on the gain of the amplifier.The gain drops from 15.1 dB at 25 °C to 13.6 dB at 75 °C and further to 12.3 dB at 125 °C, corresponding to an 18.5% decline over the full thermal range.

Simultaneously, the noise figure (NF) is consistently rising along with the temperature delta, that is, at room temperature, the noise figure is measured at 1.2 dB, whereas by 125°C, the noise figure value is 1.44 dB. This is due to an increased noise due to high thermal noise of active and passives components and bias drifts as a result of a temperature-dependent threshold voltage drift. The effect of this change in NF directly translates through to the signal-to-noise ratio (SNR) in RF receivers, which no doubt makes thermal-aware design of low-noise front-ends very important.

Also, the return loss (l S11|) also worsens with temperature, a sign of mismatch because there is an impedance drift at the input port. It is a challenge of RF systems that need stable input matching over wide temperature range. The absolute value of S‹sub>11</ sub> shifts from -18.5 dB at 25 °C to -14.9 dB at 125 °C, suggesting worsened input reflection performance. Lastly, power consumption shows a slight upward trend with temperature, increasing from 3.1 mW to 3.3 mW, which, while small, may become significant in thermally constrained environments.

The results are summarized in Table 1, with corresponding visual trends presented in Figures 5 and 6, which were generated through automated MATLAB post-processing.

The proposed thermal co-simulation methodology relies on a well-choreographed tool chain which incorporates sophisticated electrical simulation accuracy, adaptive information processing, and scripting. All these parts have their role in promoting effective simulation flow, temperature sweep regulation, and performance metric extraction under an extensive temperature range.

The electrical simulation is performed on a transistor level with Synopsys HSPICE 2023.3 version, which is an industry-standard circuit simulator commonly used in the industry and based on the fact that it is able to achieve decent convergence performance and high accuracy during RF and analog circuit simulation. BSIM3 device model provided by foundry in 0.18 pm CMOS technology is used to model the realistic temperature dependent behavior in MOSFET devices. HSPICE is a backend program that can take the .sp netlist file and calculate such outputs as gain, noise figure (NF), and return loss (S11) and can calculate power consumption.

The MATLAB R2024a is applied to control the temperature, process the results, and visualize them. Scripting in MATLAB can enable batch operation, automation of input/output of files and dynamically generate visualization of the simulation outcomes. It is also a high level controller which takes turns to inject the temperature data into the HSPICE netlist and takes out the tracked performance values to analyze the trends. The multi-point simulation, curve fitting, and the publication of the results could be done using the vectorized data structures in MATLAB.

Its inner automation is a Python 3.x script (a wrapper), which will serve as a means of communication between the MATLAB and HSPICE. In the script the subprocess package of Python is used to call a command line procedure to run the HSPICE and the script waits until the simulation is complete. When it is done, it reads the output file according to custom regular expressions or strings search pattern to extract electrical parameters. These values are then transferred to MATLAB in real time and thus putting a closure to the simulation-feedback loop. The design is flexible, modular and easy to extend to other types of simulation scenarios or circuits.

Although the study itself is ran in a fully synthetic environment, the toolchain is designed to allow the extension with Hardware-in-the-Loop (HIL), in the future. It is possible to validate thermal behavior with real measurement data in such a setup by application of a programmable testbench, like a temperature-controlled RF test chamber or Zynq-based RF signal processing prototype. This would facilitate a physical evidence to refute thermal drift in practical RF components and help develop models of temperature precision.

To conclude, the toolchain runs a first-rate and scalable co-simulation process integrating model force of HSPICE, processing calibre of MATLAB and automation quality of Python. Reproducibility, effective implementation, and scaling to higher levels by convergence of a more complex thermal-aware RF circuit design and evaluation, are guaranteed here in this modular design.

The present paper suggests a systematic co-simulation platform based on the combination of HSPICE and MATLAB that can offer thermal-aware modeling, simulation and thermal performance analysis of RF cells. The proposed system can be effectively used to automate the temperature sweeps, track the real-time performance, and visualization of key RF parameters like the gain, noise figure (NF), return loss (S11), and power consumption, thereby providing efficient automation of the temperature sweep, real-time performance tracking, and visualization of key RF parameters including the gain, noise figure (NF), and the return loss (S11), as well as the power consumption.

The main results of the works conducted prove that the thermal fluctuations may result in a regime with serious performance degradation of RF front-end circuits. A case study with 2.4 GHz CMOS-based Low noise amplifier (LNA) indicates a 18.5 percent and 20 percent decrease in gain and increase in noise figure at higher operating temperature by 25oC measure to 125oC. Also, losses of input voltage are degraded by impedance mismatch at thermal drift, and power dissipation has a very limited but measurable increase. These ramifications underscore the imperative of the capability to integrate temperature dependency in the RF design and verification process, particularly high-reliability design operating environments in 6G wireless communication, automobile radar, and biomedical telemetry systems.

The discussed framework has such value as it offers a versatile, scalable and reusable simulation infrastructure. It fills the gap of standalone SPICE simulations and the post layout thermal analysis tools and will provide a unified solution which is efficient and flexible. With the addition of a Python automation layer, reproducibility is helped and its fast design exploration allows it to be used in both academic research and in industry.

In the future, there are some directions which can be further developed concerning the potential of this framework. First, layout-aware self-heating models which can be integrated with ANSYS ICEPAK or COMSOL can be used to give a finer geometrical characterization as part of a post-layout verification. Second, the model can be applied to multi-stage RF sub-systems like mixers and power amplifiers in order to investigate the effect of cumulative thermal situations. Third, machine learning models can be integrated to extrapolate the thermal trends on a typology of simulation data, which would speed up the exploration of the design-space. Lastly, it is possible to present a hardware-in-the-loop (HIL) experiment with real-time measurements, where temperature control is performed, to ensure simulation forecasts and profile adjustments to thermal modeling through actual operating conditions.

Finally, the HSPICE-MATLAB co-simulation platform presented in this paper offers a handy and foolproof set of capabilities to RF circuit designers and engineers who seek to come up with thermally resilient circuits. Through inclusion of automation, temperature-sensitive modelling, and multi-domain exploration, the framework assists in addressing one of the key gaps within a RF circuit design flow process that has been traditionally hard to bridge, i.e. the creation of simulation closer to real deployment conditions.

Although the present work has succeeded in proving the possibility and benefits of combining HSPICE, and MATLAB to achieve thermal-aware RF circuit simulation, a number of extensions and advances can add tremendous value to the utilization and accuracy of the suggested framework. The first follow-up is the incorporation of layout-sensitive self-heating through multiphysics high-end simulation, e.g., via COMSOL Multiphysics or ANSYS ICEPAK. With these, localized temperature gradients, localized thermal hotspots, and substrate heat spreading effects can be simulated which cannot be simulated in uniform temperature SPICE. One may consider geometric placement and metal density as they may import post-layout data (e.g., a GDSII or DEF file) into the simulation flow and allow the designers to explore spatial thermal profiling block and chip-level. This will increase confidence of the framework in predicting temperature induced reliability problems like electromigration and thermal runaway in compact devices operating at high power levels in the RF band.

The other area that should undergo development is how the architecture will be implemented to multi-stage RF sub-assemblies which hold mixers, power amplifiers (PAs), voltage-controlled oscillators (VCOs). These elements are thermally and electrically interactive with each other so that the heat produced by one stage can have an effect on the bias point and impedance matching of other stages. Through this modeling of such interdependencies, the framework can develop into an integrated system-level thermo analysis and optimization tool of RF chains that is of additional importance especially in an integrated transceiver system in 5G/6G and radar applications.

One of the other promising directions is the implementation of AI/ML-based thermal prediction systems. Very little information can be collected in reduced temperature points, allowing regression models and neural networks or Gaussian process models to be trained using the limited data to provide values of their values of the performance metrics at every point on the complete thermal range. These approximations are known to save much on-computational time without compromising the required accuracy. Further, adaptive temperature sweeps may be directed by reinforcement learning agents, targeting the simulation with high sensitivity, e.g. around gain roll-off or NF spikes.

Moreover, to make the simulation result closer to the real test scenario, it is possible to add the Hardware-in-the-Loop (HIL) validation framework. The interface of a simulation loop at a controlled temperature can characterize the simulation correctness to bring it to the real-time RF expertise board (e.g. NI, PXI, Keysight, B2900A or Zynq-based SDR systems), where simulation parameters are calibrated to it. This would also justify the relevance of the framework to industrial prototyping and regulatory testing process.

In short, future work in this framework will be directed towards increasing the scale of the accuracy, execution efficiency, and interposing the disparity between simulating and conducting the actual experiment. Such advancements will not only render this framework to be a prized academic research-centric instrument, but also a highly potent driver of the RF product design; performance optimization; and qualification of the product as simulated under thermodynamically time-varying operating circumstances.