TES Readout Implementation

This page provides an overview of the status and inner workings of the TES Readout development and eventual implementation.

Description of TES

TESMap.pdf

Configuration Steps

The following are an overview of the main configuration steps for the TES-SMURF detector to go from turn the system on and start collecting data.

To be completed as more information becomes available.

1. Configure the main registers of the SMURF board (to be detailed)
2. Set the bias of the cryogenic amplifiers. The bias are provided by the manufacturer. 
3. Find the resonator frequencies - 
   1. Each multiplexed line must be scanned in frequency. The resonators are situated where the amplitude of the signal drops and there is a phase discontinuity. The phase discontinuity is used to determine the most precise frequency of the resonators. If 2 resonators are too close, one needs to be turned off to avoid crosstalk.
4. SetupNotches - Find the transformation η(I,Q) = (I', Q') where η is a complex transformation. We want to move the operation point so that we are at the (I', Q') = (1,0) point and that all variations maximized their projection on Q'. 
5. Setup the flux ramp. The Squids are connected to the flux generators in large groups. Each group can have a different flux ramp.
   
   1. Flux amplitude
   2. Ramp amplitude
6. At this point it is possible to track the phase of the carrier frequency and collect data but the TES still need to be configured.
7. The voltage for the TES is selected by tracing the IV curve, from which we get the RTES curve. We wish to be as close as possible to the bottom part of the transition without hitting instability effects.

Benchmarks

This table lists the latest benchmarks.

Currently, it evaluates the average rate (over 1e6 iterations) to complete one linear transformation and two inner products. Each pixel has its own ring buffer of 10 windows, each window has 100 data samples of uint_16. The buffers are refilled as the reader nears the empty flag from Random data. The buffers are implemented using Eigen Matrix library.

These benchmarks were obtained os psanagpu116, using O3 and vectored optimization. As a reference, 10 kHz operation gives a time window of 100 µs.

g++ -std=c++11 -O3 -DNDEBUG -march=native -IEigen main.cpp

The following graph shows the calculation rate per pixel as a function of the density of hits. While the data generation is not in the scope of the timer, it may still affect the start and stop of said timer. Clemens algorithm pre generates all data and thus times one continuous loop. Thus if there are no events, the loop is very very efficient. However, the Eigen library used in the most recent version of the code does accelerate the calculation of the inner products, as shown by the better performance at higher hit rates. 

Latest code :

Latest integration iteration (Working copy)

Original codes before combinations.

Code with Eigen (Audrey)

Code with switch case (Clemens)

References

Ullom and Bennet. Review of superconducting transition sensors for X-ray and gamma-ray spectroscopy

Irwin and Hilton. Transition-edge sensors.

Tasks