Testbeam results of the Picosecond Avalanche Detector proof-of-concept prototype

The proof-of-concept prototype of the Picosecond Avalanche Detector, a multi-PN junction monolithic silicon detector with continuous gain layer deep in the sensor depleted region, was tested with a beam of 180 GeV pions at the CERN SPS. The prototype features low noise and fast SiGe BiCMOS frontend electronics and hexagonal pixels with 100 μm pitch. At a sensor bias voltage of 125 V, the detector provides full efficiency and average time resolution of 30, 25 and 17 ps in the overall pixel area for a power consumption of 0.4, 0.9 and 2.7 W/cm2, respectively. In this first prototype the time resolution depends significantly on the distance from the center of the pixel, varying at the highest power consumption measured between 13 ps at the center of the pixel and 25 ps in the inter-pixel region

Picosecond Avalanche Detector — working principle and gain measurement with a proof-of-concept prototype

The Picosecond Avalanche Detector is a multi-junction silicon pixel detector based on a (NP)drift(NP)gain structure, devised to enable charged-particle tracking with high spatial resolution and picosecond time-stamp capability. It uses a continuous junction deep inside the sensor volume to amplify the primary charge produced by ionizing radiation in a thin absorption layer. The signal is then induced by the secondary charges moving inside a thicker drift region. A proof-of-concept monolithic prototype, consisting of a matrix of hexagonal pixels with 100 μm pitch, has been produced using the 130 nm SiGe BiCMOS process by IHP microelectronics. Measurements on probe station and with a 55Fe X-ray source show that the prototype is functional and displays avalanche gain up to a maximum electron gain of 23. A study of the avalanche characteristics, corroborated by TCAD simulations, indicates that space-charge effects due to the large primary charge produced by the conversion of X-rays from the ^55Fe source limits the effective gain

New insights into the genetic etiology of Alzheimer's disease and related dementias.

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine

Radiation tolerance of SiGe BiCMOS monolithic silicon pixel detectors without internal gain layer

A monolithic silicon pixel prototype produced for theMONOLITH ERC Advanced project was irradiated with 70 MeV protons upto a fluence of 1 × 10$^{16}$1 MeV n$_{eq}$/cm$^{2}$. The ASIC contains a matrix ofhexagonal pixels with 100 μm pitch, readout by low-noise andvery fast SiGe HBT frontend electronics. Wafers with 50 μmthick epilayer with a resistivity of 350 Ωcm were used toproduce a fully depleted sensor. Laboratory tests conducted with a$^{90}$Sr source show that the detector works satisfactorily afterirradiation. The signal-to-noise ratio is not seen to change up tofluence of 6 × 10$^{14}$n$_{eq}$/cm$^{2}$. The signaltime jitter was estimated as the ratio between the voltage noise andthe signal slope at threshold. At -35°C, sensor bias voltageof 200 V and frontend power consumption of 0.9 W/cm$^{2}$, the timejitter of the most-probable signal amplitude was estimated to be σ$_{t}$$^{90}$Sr = 21 ps for proton fluence up to6 × 10$^{14}$n$_{eq}$/cm$^{2}$ and 57 ps at1 × 10$^{16}$n$_{eq}$/cm$^{2}$. Increasing the sensorbias to 250 V and the analog voltage of the preamplifier from 1.8to 2.0 V provides a time jitter of 40 ps at1 × 10$^{16}$n$_{eq}$/cm$^{2}$.A monolithic silicon pixel prototype produced for the MONOLITH ERC Advanced project was irradiated with 70 MeV protons up to a fluence of 1 x 10^16 1 MeV n_eq/cm^2. The ASIC contains a matrix of hexagonal pixels with 100 μm pitch, readout by low-noise and very fast SiGe HBT frontend electronics. Wafers with 50 μm thick epilayer with a resistivity of 350 Ωcm were used to produce a fully depleted sensor. Laboratory tests conducted with a 90Sr source show that the detector works satisfactorily after irradiation. The signal-to-noise ratio is not seen to change up to fluence of 6 x 10^14 n_eq /cm^2 . The signal time jitter was estimated as the ratio between the voltage noise and the signal slope at threshold. At -35 {^∘}C, sensor bias voltage of 200 V and frontend power consumption of 0.9 W/cm^2, the time jitter of the most-probable signal amplitude was estimated to be 21 ps for proton fluence up to 6 x 10 n_eq/cm^2 and 57 ps at 1 x 10^16 n_eq/cm^2 . Increasing the sensor bias to 250 V and the analog voltage of the preamplifier from 1.8 to 2.0 V provides a time jitter of 40 ps at 1 x 10^16 n_eq/cm^2