Nanowire Sensor Architecture

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 :100’s or 1000’s 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 scheme—basic 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!