Development of deep N-well MAPS in a 130 nm CMOS technology and beam test results on a 4k-pixel matrix with digital sparsified readout

We report on further developments of our recently proposed design approach for a full in-pixel signal processing chain of deep n-well (DNW) MAPS sensors, by exploiting the triple well option of a CMOS 0.13 μm process. The optimization of the collecting electrode geometry and the re-design of the analog circuit to decrease power consumption have been implemented in two versions of the APSEL chip series, namely “APSEL3T1” and “APSEL3T2”. The results of the characterization of 3x3 pixel matrices with full analog output with photons from <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">55</sup> Fe and electrons from <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">90</sup> Sr are described. Pixel equivalent noise charge (ENC) of 46 e- and 36 e- have been measured for the two versions of the front-end implemented toghether with signal-to-noise ratios between 20 and 30 for Minimum Ionizing Particles. In order to fully exploit the readout capabilities of our MAPS, a dedicated fast readout architecture performing on-chip data sparsification and providing the timing information for the hits has been implemented in the prototype chip “APSEL4D”, having 4096 pixels. The criteria followed in the design of the readout architecture are reviewed. The implemented readout architecture is data-driven and scalable to chips larger than the current one, which has 32 rows and 128 columns. Tests concerning the functional characterization of the chip and response to radioactive sources have shown encouraging preliminary results. A successful beam test took place in September 2008. Preliminary measurements of the APSEL4D charge collection efficiency and resolution confirmed the DNW device is working well. Moreover the data driven approach of the readout chips has been successfully used to demonstrate the possibility to build a Level 1 trigger system based on Associative Memories.