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Beratung zu Yield und Datenanalyse

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App­li­ca­ti­ons for machi­ne lear­ning and Arti­fi­cal Intelligence

What we mean

Tog­e­ther with you we defi­ne if your use cases are sui­ta­ble for app­li­ca­ti­on of machi­ne lear­ning and neural nets. Examp­les are auto­ma­ted clas­si­fi­ca­ti­on of defect images and wafer signa­tures, detec­tion of ano­ma­lies and dis­co­very of con­nec­tions bet­ween pro­duc­tion rela­ted data like material‑, quality‑, sensor‑, tool‑, tracking data.

How we do it

We train you on the necessa­ry base for under­stan­ding the algo­rithm. In work­shops we dis­cuss with your pro­cess experts and tech­no­lo­gists the pos­si­ble app­li­ca­ti­ons. With our ‚Ana­ly­tic Lab‘ we can test and veri­fy  your use cases. Through opti­miz­a­ti­on of algo­rithm pro­to­ty­pes will be deve­lo­ped. Tho­se can show the gene­ral poten­ti­als of your app­li­ca­ti­ons as well as being fea­si­ble for use in pro­duc­tion. After suc­cess­ful pro­ve of con­cept pha­se app­li­ca­ti­on can also be imple­men­ted real time.

  • Yield/Quality
  • Con­sul­ting
  • Data Inte­gra­ti­on
  • Imple­men­ta­ti­on
  • Orga­niz­a­ti­on
  • Data Ana­ly­tics
    • Requi­re­ment analysis 
    • Defect Engi­nee­ring
    • Pro­cess con­trol methodology 
    • Yield cor­re­la­ti­on and pre­dic­tion, other methods 
    • Signa­tu­re- und image analysis 
    • Machi­ne Lear­ning / Arti­fi­cal Intelligence 
    • Per­so­nas and interfaces 
    • Sus­tainab­le change 
    • Con­va­nit Ana­ly­tics Lab 
    • Pro­to­typ­ing 
    • Imple­men­ta­ti­on & Deploy­ment Support 

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