Plateformes à TIMA


The Laboratory uses extensively the microelectronics software tools and design environments available via the design platform of CIME Nanotech in the Minatec Campus ( The researchers of the Laboratory are involved in the Board of Directors and the operation of several platforms of this joint research and education center. We also use shared platforms within the FMNT federation of Grenoble Laboratories ( We describe below some of the experimental platforms that we have in the Laboratory.

CDSI team

The CDSI team owns testing equipment and has access to a fabrication facility needed for micro-transducer developments. These facilities are located at CIME Nanotech. The TIMA Laboratory has extensive experience in MEMS-based sensors, especially with electro-acoustic and electro-mechanical transducers in different application fields. Indeed, we have developed advanced testing environments:

Benches for MEMS characterization.

RIS team

Neutrons and other energetic particles present in the Earth's atmosphere can challenge the reliability of electronic systems operating in the Earth's atmosphere. In particular, SRAMs can be affected by bit-flips that corrupt the stored information. In the past this was strictly considered for systems working at very high altitudes (airplanes, satellites …) but the advances in manufacturing technologies makes present and future integrated circuits sensitive to this kind of atmospheric radiation even when they operate at ground level. This problem must be taken into account for any application in which errors may have critical consequences

The SRAMCHECKER experimental platform includes 1Gigabit memory built from commercial SRAMs and two FPGAs, one used for memories addressing and the other to control the test strategy and communicate with the user. This platform is used to get an objective feedback concerning the sensitivity to Single Event Upsets (SEUs) of commercial SRAMs issued from different manufacturing technologies generations. The architecture includes 64 SRAM memories placed on both sides of the PCB, control logic, clocks and power supplies and an external Arduino processor with a GSM module which is programmed both to on-line communicate to the user, through a website, the errors detected in the platform and to update the error data base on the server.

SRAMCHECKER has been largely used in the frame of various projects of RIS team. Results issued from its activation at high altitudes (commercial flights, balloons and high mountains) provided objective data concerning the issues related with radiation effects in integrated circuits. Moreover, the detection of multiple faults in adjacent memory cells, certainly dues to the impact of a single particle, motivated the manufacturer to include suitable techniques, such as “bit interleaving” to deal with them.


RMS team

As part of the collaboration with the company Xdigit within the Pyxcad joint research laboratory, the RMS team has acquired an Applicos ATX7006 test bench in 2018 which makes possible the testing and characterization of very high-resolution ADCs. The ATX7006 consists of several specific modules that perform functions such as:


Applicos ATX7006 ADC characterization test bench, and (b) FPGA-based environment for image sensor quality evaluation.

Within the RMS team, we have developed an experimental platform built around an FPGA that allows us to perform image sensor reliability and robustness functions through an analysis of product files. On this platform, the following functions are available:

SLS team

As the group works at the HW/SW interface, our platforms are generally combination of HW components realized on FPGA and SW components. The platform put in place for the Ternary Neural Network (TNN) proof of concept is a typical platform of the group. On top, classical software development to generate files that instance hand written VHDL models, either ours or off-the-shelf, and develop ad-hoc Linux drivers for our designs. On the right, an FPGA board inserted into a PC and connected to it via a PCI express port. For the very case of the AI accelerator, it is a Xilinx VC709 board, one of the largest when we started the project. The picture on the right is the actual platform.

Hardware/software platform for Ternary Neural Network (TNN) proof of concept.