Special features and applications using pyBAR are listed here. Corresponding code is e.g in the example folder or the links are mentioned.

Content:

• Charge reconstruction method to get single pixel charge spectra
• Shift the threshold while measuring the particle rate to record the integrated charge spectrum
• Noise < 200 e
• Statistical method, no single hit charge
• Published method

Use calibrate_threshold.py to create a threshold calibration per pixel. Then put a source on the FE or go to a test beam and run scan_ext_trigger_gdac.py. For the analysis of the data use analyze_source_scan_gdac_data.py.
 

 

Content:

• Charge reconstruction method to get single pixel charge spectra
• Use the FPGA TDC to digitize the TOT-Signal with a 640 MHz clock
• Noise 300 – 1000 e, depending on the tuning
• Single pixel hit charge can be measured

Use 2 open drain buffers to amplify the Hit-Bus signal and modify the MIO to translate the H/Z signal to H/L:
 

 
Use calibrate_hit_or.py to create a TDC calibration per pixel. Then put a source on the FE or go to a test beam and run scan_ext_trigger.py with enable_tdc: True. For the analysis of the data use analyze_source_scan_tdc_data.py.
 

 

Content:

• Shows how to use write a prim list
• Chip parameters are changed within a loop
• Results are analyzed and plotted within the same script

A prim list prim_list_tdc_sn.py is used to change the parameters that change the signal-to-noise of the TOT-Signal: discriminator bias, feedback current and threshold (DisVbn, PrmpVbpf, Vthin_AF). One can see that low threshold / low feedback / high discriminator bias tunings give a better S/N.