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Nanowire Sensor Architecture

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Presentation Transcript

    Slide 1:Nanowire Sensor Architecture Wei Xu Computer Science and Engineering Penn State University

    Slide 2:Outline Nanowire sensor array A/C conversion Pre-processing architecture Pattern recognition algorithm

    Slide 3:Nanowire sensor Array Electronic noses: cross-reactive arrays that report accurately on the concentration of analytes in complex mixtures by virtue of the varied response of different sensor elements. Applications: . detecting hazardous emissions from chemical plants . detection and control of automobile emissions . odor control in chemical and food processing . fire detection

    Slide 4:Various forms of chemical sensor Mass-sensitive: ?f~ ?Cgas Capacitive: ?C ~ ?Cgas Calorimetric: Utherm ~ ?Cgas/ ?t Chemoresistors: ?R ~ ?Cgas

    Slide 5:Going from macro to nano scale low cost: batch fabricated low power consumption: Individual nanowires will dissipate on the order of tenths of mW; long term battery-powered operation becomes possible for small arrays massive redundancy :100s or 1000s of sensors; power-intensive with macroscopic devices

    Slide 6:A/D Conversion --Current Comparator

    Slide 8:Current Mirror

    Slide 9:Current Comparator with 2 bit resolution, 3 sensing levels

    Slide 10:Raux=1.2K O, VDD=3.7V, Iref0=0.31mA, Iref1=0.287mA; Assume Rsensor=3K O (no gas)?R=0.3K O (gas exists, less confident) ?R=1K O (gas exists, more confident)

    Slide 11:?R=300K O, strong signal, less buffer

    Slide 12:Pre-Processing Architecture--Scheme 1

    Slide 15:Observations Yield of nanowire is currently low Yield of alignment is currently low Process data from working sensors only Solution: wield away bad sensor inputs by adding a mask signal to each memory register

    Slide 16:Observations After processing, the value in each PE ranges between 0-4 . small range, prone to noise . need 3 bits to represent ? Each PE processes 7 sensors

    Slide 17:New schemebasic module

    Slide 18:Mesh-connected Module (MCM)

    Slide 19:Operation Step 1: training ? mark all the bad locations Step 2: computing signature feature vector Step 3: processing data, get feature vector Step 4: final pattern recognition (compare feature vector with signature feature vector), using Least Square Estimation

    Slide 20:Step 1: training

    Slide 21:Step 2: signature feature vector

    Slide 22:Step 3: sensing

    Slide 23:Step 4: pattern recognition-Least Squares Approach

    Slide 24:Apply LSE to our problem (1-0)2+(2-0)2+(1-0)2+(1-0)2= 7 (1-3)2+(2-5)2+(1-6)2+(1-3)2=42 ?Not exist

    Slide 25:Future work Initial synthesis using TSMC 0.25um library shows the chip with 256 sensors and 64 processing elements running at 200MHz With improved yield, find suitable aggregation method and better pattern recognition scheme

    Slide 26: Thank you!