In this work, the relationship between Lyapunov exponents, used to estimate the effects of process variability, and the standard deviation obtained through the Monte Carlo method was established analytically, demonstrating that the Lyapunov exponents characterize a metric for estimating such effects. Based on this, a new metric for estimating the effects of process variability, based on Lyapunov exponents, was developed. The Sum-Power-Delay (SPD) was analytically defined, and its application was verified in the design of an inverter using 180nm technology. The SPD was applied to compare two sets of twenty cells designed in 180nm and 180nm-RH, a radiation-tolerant cell library developed at the Santa Maria Design House (SMDH), affiliated with the Federal University of Santa Maria (UFSM), demonstrating that circuits designed with Enclosed Layout-Transistor (ELT) are more robust against process variability. These results were obtained by analyzing circuits from the ISCAS85 benchmarks, as well as twenty circuits synthesized with our library. The same analysis was performed for matrix multiplication, aiming to evaluate the impact of process variability on this fundamental operation in space-grade digital systems. Finally, the 180nm-RH library was used to estimate the effects of process variability on circuits designed for space-oriented applications, including neural networks, filters, image compression, edge detection, and RISC-V microcontroller core modules. Key circuits for space applications, including image compressors, digital filters, and edge detection modules, were synthesized using a radiation-hardened standard cell library, enabling comparative evaluation of their robustness to process variability. The analysis provides valuable guidance for selecting circuits suitable for radiation-prone environments. Extension to RISC-V cores with 180 nm radiation-hardened transistors showed that Pico32 had the highest tolerance to process variability, confirming the practical effectiveness of the SPD metric in predicting and mitigating variability effects across designs.

The objective of this work is to unify, with mathematical rigor, the analyses to estimate the effects of process variability and the effects caused by radiation in digital circuits using a single analysis. First, we proved that to estimate the effects of process variability on delay and power, it is enough to estimate at least one of the two. Later, a relationship between the effects of radiation and the effects of variability was obtained through a common element between the two: the entropy of the circuit. We applied the presented methods to compare circuits with the operations: matrix multiplication, graph searches, encryption algorithms, and neural networks. All of the circuits are designed using a 180 nm technology. We were able to verify which circuits are more sensitive to the effects of variability and radiation simultaneously through the proposed method.

With the technology evolution, the decrease of transistors size and, thus, the increase of density of transistors per area, System-on-a-chip (SoC) development is becoming more and more complex. If design, verification, and test are considered, it is noticed how challenging it is getting for designers to plan and manage chips with dozens of billions of transistors, where optimization is a keyword to cope with the IoT world. This paper presents the use of a brand-new tool called ALTRAN to automatically generate the layout of chips using an FDSOI technology.

The goal in the present paper is to propose, develop and show a method for classifying the spatial cognition degree of development on different individuals. Thirty-Seven undergraduate students had their electroencephalogram (EEG) recorded while engaged in 3-D images mental rotation tasks. Their spatial cognition degree of development was assessed using a BPR-5 psychological test. The Largest Lyapunov Exponent (LLE) was calculated from each of the 8 electrodes recorded. The LLEs were used as input for three different classifiers: i) multi-layer perceptron artificial neural network, ii) support vector machines, and iii) K-Nearest Neighbors. The best result was achieved by the MLP using the tansig transfer function. The proposed classification method can contribute and interact with other processes in the analysis and study of human spatial cognition, as in the understanding of the human intelligence at all. A better understanding and evaluation of the cognitive capabilities of an individual could suggest him elements of motivation, ease or natural inclinations, possibly affecting the decisions of his carrier positively.