2023-06-22 22:00:00
The market for electronic chips continues its strong expansion due to the increase, among other things, in the demand for electronic communication devices, autonomous means of transport, connected objects, mass data processing (big data) and the internet of things.
This demand has led, for decades, to a race to miniaturize transistors which has had the beneficial effects of increasing computing power while reducing size, consumption and unit costs in a virtuous circle leading to investments and strong returns. However, the pursuit of this aggressive strategy of downscaling and particularly the descent into nanometric dimensions, reveals new challenges and pushes design and verification tools to reinvent themselves. The sizes of the circuits to be simulated, given the increase in the number of components and interconnections, become critical. The surface densities of dissipated powers become so important that their impacts on the operation of the devices and their lifetime must be effectively taken into account. This is true for all electronics applications, but remains a sensitive subject particularly in transport, medical or military applications, for which reliability is a primary factor. Similarly, sensitivity to variations in manufacturing processes is becoming so important that taking it into account is a key factor in reducing production costs. Another effect of the descent into nanometric dimensions is that of the drastic increase in production costs, given the complexity of the manufacturing processes themselves, pushing manufacturers to invest massively in production units, in order to reduce unit costs.
It is in this context that this article comes to expose how the AMS/RF simulator, present both in the design phases and in those of verification of the circuits, is a determining factor on the economic level, to reduce the times of implementation. market and to ensure robustness and reliability, while improving production yields. This article therefore focuses on methods for accelerating the performance of the AMS/RF simulator, then on statistical methods for analyzing process variability, and finally on methods for obtaining a reliable and robust circuit design. The effectiveness of these methods partly depends on the possibility of being able to continue miniaturization, which increases performance while lowering consumption and unit cost prices.
The reader will find a glossary and a table of acronyms used at the end of the article.
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