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Mentor joins Nano 2022 as France focuses on microelectronics

lanka 2020-11-21 11:10:45

EDA company, Mentor, has joined Nano 2022, the latest project to promote the French electronics manufacturing industry. It covers components, connectors and PCBs, to design and assembly, distribution, embedded software and software tools. The project is part of the European Commission’s Important Project of Common European Interest (IPCEI) to promote research and innovation in power ICs, sensors, optical equipment and compound materials for automotive, security, IoT, space and avionics industries.

Machine Learning with technology and engineering concept. Human head (profile) with a circuit brain board inside and Machine Learning logo. Selective focus

Mentor will collaborate with STMicroelectronics, another Nano 2022 member,  to provide design and verification for low power chips, power semiconductors and other circuit architectures which need fast, accurate and high capacity circuit simulators for pre-layout and post-layout circuits. The two companies have a partnership history, with  ST’s Bulk CMOS, FDSOI, analogue and RF, embedded non volatile, imaging and Bipolar CMOS-DMOS or BCD and other technologies. Mentor’s nanometer circuit verification Analog FastSPICE Platform and analogue-centric Eldo software cover all the circuit types and technologies in the Nano 2022 program.

The Mentor-ST collaboration will also include the characterisation of standard cells, I/Os and memories. This task can can create a bottleneck in production, says Mentor,  as characterising silicon platforms with hundreds of cells and several hundred process, voltage and temperature (PVT) variables can consume thousands of CPUs for weeks, running millions of SPICE simulations. Mentor’s Solido Characterization Software Suite, is a machine learning-powered tool that increases the throughput while producing accurate Liberty files and statistical data, says the company. It also provides tools and a designer-centric user interface to verify the Liberty files. New reinforcement-learning techniques can be developed during the program to advance characterisation.