Patent Application: US-9182906-A

Abstract:
the present invention provides an associative memory device based on a ferromagnetic nano - colloid , or ferrofluid . the design comprises inductive input and output units for training the ferrofluid as well as sensors incorporated into the output units for performing recall .

Description:
embodiments of the invention will now be described with reference to the figures . the associative memory device 100 according to the invention is schematically illustrated in fig1 . and comprises a ferrofluid layer 105 , i . e . magnetic nanoparticles in a layer of solvent , providing the magnetically responsive layer . the solvent may be water , oil , paraffin , etc and have a viscosity of which preferably should be variable by small changes of temperature near room temperature . the magnetic nanoparticles , which are the magnetically active component in the ferrofluid , correspond to the “ hidden units ” in the nn analogy , and the dipolar interaction between the particles can be seen as the synaptic weight . the particles in the fluid are typically ferromagnetic and of small size ( 10 nm ), such that the magnetization is single domain [ 6 ]. below the ferrofluid layer 105 is a thin bottom plane 110 . on the bottom plane are a plurality of multilayered inductive elements provided [ 12 ]. the inductive elements , or pads , are arranged pairwise with input inductive elements , hereafter referred to as input pads 115 and output inductive elements , hereafter referred to as output pads 120 , and designed to produce desired local magnetic fields corresponding to the patterns being stored or recalled . the generic layout of a block of four cells is shown in fig1 . each input pad ( i ) 115 has 4 nearest neighbour output pads ( o ) 120 . all pads are placed under a thin bottom plane . the pads are preferably tri - layers with the top and bottom magnetic films separated by a metal spacer , in which a controlling current can switch the pad into the parallel ( p ) or antiparallel ( ap ) magnetic configuration , corresponding to strong and weak local fringing fields , respectively . the proposed layout of the input pad is shown in fig2 . the structure can be designed [ 12 ] to produce high local fringing fields in training ( p - state ) and low fields in recall ( ap - state ). the low fields during recall are achieved in the ap state of the asymmetric in thickness tri - layer , with most but not all of the flux closed within the pad . the output pads have a symmetric in thickness tri - layer ( not shown ), so that all flux is closed in the ap state of the o - pad during recall . the output pads are designed to additionally perform sensing of the local fields during recall using the spin - valve property of the magnetic sandwich [ 13 , 14 ]. according to the invention the ferrofluid layer 105 is trained to store patterns by applying input - output field sets using the input / output pads 115 / 120 . each cell of the proposed device contains one input and one output pad , and a large number of magnetic dipoles (≈ 10 3 ), assumed to have a variable mobility . the mobility of the particles in the ferrofluid can be controlled by varying the temperature of the solvent in the vicinity of room temperature . during the application of the input / output pattern pair the magnetic nanoparticles in the colloid are allowed to move and their moments are allowed to rotate to minimize the total magnetic energy of the system . the above training sequence can be accomplished by switching the appropriate input / output pads 115 / 120 into their p states ( p - left or p - right , corresponding to digital ‘ 0 ’ or ‘ 1 ’, see fig2 ). the particles are allowed to move and their magnetic moments rotate until the system reaches equilibrium , at which point the particle locations are fixed by changing the viscosity of the solvent , for example by lowering the temperature . pattern recall is done by allowing the magnetic moments of the nanoparticles to rotate ( but not move ) in response to a given input . the input elements receive the same input pattern as the one that was used in training , but now the input pads 115 are switched into the antiparallel state in order to reduce the direct influence of the input fringing fields on the output pads 120 . during recall , the output pads 120 are used in a field sensing rather than field setting mode . for this the output pads 120 are also set into the ap state . symmetric ( see design ) with zero fringing fields . the field sensing property of the output pad magnetic tri - layer ( known as the spin - valve [ 13 ]) serves to read out the effective dipolar fields produced by the local ( immobilized ) pattern of nano - dipoles . the training sequence according to the method of the invention comprises the steps of : a . imposing simultaneously an input and an output pattern , by exposing the ferrofluid layer with localised magnetic fields , wherein a first set of fields corresponds to the input pattern , and the second corresponds to the output pattern ; b . letting the magnetic nanoparticles of the ferrofluid equilibrate with respect to particle positions and spin directions ; c . fixing particle positions . the recalling sequence according to the method of the invention comprises the steps of : d . imposing the input pattern to the ferrofluid layer ; e . letting the spin orientations equilibrate ; f . sensing the magnetic field from the ferrofluid layer . the present invention has been illustrated with a single pattern storage and recall . the associative memory device and method according to the invention could easily be adapted for multiple pattern storage and recall . given that the dipolar interactions decay relatively quickly with distance , the memory function is to a large limited to the nearest neighbour cells . extending the proposed design to recalling multiple patterns will likely involve increasing the particle / pad dimensional ratio compared to the single pattern case . the use of the associative memory device and the method according to the invention is further illustrated by the below described simulation : since the pads are much larger than the particles , they cannot be modelled magnetically as dipoles . instead , the interaction of the nanoparticles with the pads is calculated by meshing the pads into small elements ( effectively concentrated at the ends of the pads ) and computing the fields at the particle locations from such elements to obtain the zeeman pad - particle contribution to the total energy . the typical cell size is 500 nm corresponding to 30 particle diameters . the i / o patterns were up to 6 × 6 cells . the ‘ hidden units ’ in the proposed design ( neurons in the nn case ) are magnetic nanoparticles in a thin layer of a solvent ( water , oil , paraffin , etc . ), the viscosity of which should be variable by small changes of temperature near room temperature . what is know in nn &# 39 ; s as synaptic weights , mediating the inter - neural interactions , are the inter - particle magnetic dipolar interactions . the particles in the fluid are taken to be ferromagnetic and of small size ( 10 nm ), such that the magnetization is single domain [ 6 ]. each particle is characterized by its magnetic moment , m ( r ), which can change its direction and position in the fluid film but not the magnitude . the particles are assumed to be coated with a 2 nm thick anti - agglomeration layer , which sets the shortest interparticle distance for the simulations (≈ 4 nm ). the complete description of ferrofluids within the framework of statistical mechanics includes many contributions from the various interactions in the liquid . however , effects of the carrier liquid and the surfactant can often be neglected since they can only have an indirect influence on the magnetization . a model of a ferrofluid describing both magnetic particles and the carrier fluid is discussed by kalikmanov [ 9 ], who shows that the carrier liquid has no influence on the equilibrium magnetic properties of the ferrofluid as a whole under some realistic assumptions . the nano - colloid is therefore described as a system of dipoles , i = 1 , . . . , n . each carrying a magnetic moment m i and interacting with the local effective field from the pads h e via the zeeman potential v i =− m i h e . the pairwise dipole - dipole interactions are described by the hard - shell short range potential [ 5 ] of ≈ 2 nm is used in the simulations below , so the closest distance between the surfaces of the nanoparticles is 4 nm . we perform a monte carlo simulation of the system using the cluster - moving algorithm [ 7 , 10 ], which takes into account the cooperative behavior of the particles , and the particle mesh method [ 11 ] for faster convergence . the thin ferrofluid layer is trained to store patterns by applying input - output field sets using nano - sized magnetic pads placed under the surface of the colloid . each cell of the proposed device contains one input and one output pad , and a large number of magnetic dipoles (≈ 10 3 ), assumed to have a variable mobility . the mobility of the particles in the ferrofluid can be controlled by varying the temperature of the solvent in the vicinity of room temperature . during the application of the input / output pattern pair the magnetic nanoparticles in the colloid are allowed to move and their moments are allowed to rotate to minimize the total magnetic energy of the system . the system is then cooled ( i . e ., the particles immobilized ) in this minimum total energy configuration , which includes the zeeman - like pad - particle and dipolar inter - particle contributions . the resulting ( immobilized ) network of nano - dipoles ‘ associates ’ the specific patterns imposed at the input and output by magnetically linking the i / o pads . the above training sequence can be accomplished by switching the appropriate i / o elements into their p states ( p - left or p - right , corresponding to digital ‘ 0 ’ or ‘ 1 ’, see fig2 ). the particles are allowed to move and their magnetic moments rotate until the system reaches equilibrium , at which point the particle locations are stored . the result of such training sequence for a 9 - input ( 3 × 3 ) 4 - output ( 2 × 2 ) system at 300 k is shown in fig3 . the particles are 15 nm in diameter with a 2 . 25 nm polymer shell and a magnetic moment of 1220 emu / cc . the magnetization of the i / o pad material is 1700 emu / cc ( corresponding to that of fe ). the mc simulation is 2 - d with the magnetic fraction of the ferrofluid of 15 %. the size of each cell is 620 × 490 nm 2 . the input and output pads are 80 × 210 × 40 nm 3 and 300 × 80 × 40 nm 3 , respectively . the rhombic link at the bottom center , for example , corresponds to inward directed moments of the lateral i - pads and outward directed o - pads . all in the p - state ( for layout see fig1 ). in this example , the nanoparticle mediated i / o coupling is strongest for the 4 nearest neighbour pads . a typical training simulation ran for ≈ 10 6 monte carlo steps . a modified cluster - moving algorithm [ 10 , 14 ] resulted in the fastest convergence to the equilibrium as illustrated by fig4 for a 2 × 2 system . we find , however , that the exact details of the particle random walk algorithm are not critical for the conclusions reached in this work . pattern recall is done by allowing the magnetic moments of the nanoparticles to rotate ( but not move ) in response to a given input . the input elements receive the same input pattern as the one that was used in training , but now the input pads are switched into the antiparallel state in order to reduce the direct influence of the input fringing fields on the output pads . during recall , the output pads are used in a field sensing rather than field setting mode . for this the output pads are also set into the ap state . symmetric ( see design ) with zero fringing fields . the field sensing property of the output pad magnetic tri - layer ( known as the spin - valve [ 13 ]) serves to read out the effective dipolar fields produced by the local ( immobilized ) pattern of nano - dipoles . the sensed output pattern , with its magnitude and direction , is then compared to the one imposed during training . the recall is successful if the two coincide . a pattern recognition example is shown in fig5 for a 6 × 6 input and 5 × output system trained to associate the chinese and arabic characters for digit “ 5 ”. 100 % accuracy is reached for the output sensor threshold field of 25 oe . reaching the correct output in this example required a rather short sequence of ≈ 10 3 mc steps . while the invention has been described in connection with what is presently considered to be the most practical and preferred embodiments , it is to be understood that the invention is not to be limited to the disclosed embodiments , but on the contrary , is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims . c . m . bishop , ( 1995 ). neural networks for pattern recognition , oxford university press , oxford . b . muller , j . reinhardt , & amp ; m . t . strickland , ( 2nd edition , 2002 ). neural networks : an introduction ( physics of neural networks ). springer . j . m . goodwin , b . e . rosen , & amp ; j . j . vidal , ( 1992 ). international journal of pattern recognition and artificial intelligence , vol . 6 , no . 1 , pp 157 - 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