WO2019136377A1

WO2019136377A1 - Hypercomputation with programmable matter

Info

Publication number: WO2019136377A1
Application number: PCT/US2019/012537
Authority: WO
Inventors: Bill Aronson; Edward Rietman; Yossi Avni; Eytan Suchard
Original assignee: Artificial Intelligence Research Group (Bahamas) Ltd.
Priority date: 2018-01-08
Filing date: 2019-01-07
Publication date: 2019-07-11
Also published as: WO2019136385A1; GB201800280D0

Abstract

Disclosed herein is a system that provides an entirely new way of computing. Engineered traveling or standing wave pulse patterns in certain types of materials can be interpreted as a computation. Internal pulse patterns in the materials(s) cause constructive and destructive interference and thereby manipulate internal dipoles and other supramolecular elements in a controlled way.

Description

HYPERCOMPUTATION WITH PROGRAMMABLE MATTER

BACKGROUND OF THE INVENTION

1. Field of the Invention

[0001] The present invention relates to a system that provides an entirely new way of computing. Engineered traveling or standing wave pulse patterns in certain types of materials can be interpreted as a computation. Internal pulse patterns in the materials(s) cause constructive and destructive interference and thereby manipulate internal dipoles and other supramolecular elements in a controlled way.

2. Technical Field

[0002] The invention disclosed herein exploits the electrical and acoustic properties of piezo materials for computation. Therefore, the background involves materials science, solid-state electronics and acoustics. In the Detailed Description section, we also outline new materials and electronic applications thereof.

[0003] Certain materials, such as: PVDF, PVA and PZT may be exploited for computation. Basically, blocks of these substrates become programmable matter. One key feature of the programmable matter introduced here is that each instance of it, each sample, will be unique at low frequencies (for example below 10kHz in PZT), but quite similar to one another at higher frequencies. This suggests a new type of secure computing.

[0004] Aspects of the present invention relate to the electrical properties of piezoelectric materials for computation. Therefore, the background further involves materials science and solid-state physics. BACKGROUND OF MATERIALS

[0005] It is well known that polyvinylidene fluoride (PVDF), polyvinyl alcohol (PVA), and lead zirconate titanate (PZT) contain mobile dipoles. The mobility of the dipoles in the polymers is related to the molecular weight and the degree of crystallization. PVA is not a particularly crystalline polymer; PVDF is highly crystalline; and of course, in the PZT ceramic, the dipoles have even less mobility.

All three of these materials have piezoelectric properties. PVDF is used in

manufacturing of small microphone and buzzer applications. PZT is used in almost all major ultrasonic applications from medical to undersea communication. PVA is typically not used for piezoelectric applications because it is highly hydroscopic.

[0006] The crystallites in PVDF and the poly-crystals in the ceramic PZT act as supramolecular-scale capacitors (Yamagami and Fukada, 1973). The dipoles in these materials come about because of asymmetry of the positive and negative charge density in the material. The negative pole of a dipole arises because of free or unbound electrons. For example, water molecules are polar because of free electrons on the oxygen atom. The total dipole moment of a molecule is given by:

are the electron and nuclear charge densities. For a

polymer, it is a little more difficult because we need to consider the average molecular conformation and the degree of polymerization, N. In this case the square average is

where ^,li- is the moment for one monomer (e.g. Eq. [1 ]) and ^f is a constant representing the folded polymer conformation. Polyvinylidene fluoride (PVDF) has the highest dipole moment of 2.1 D (debye) of any polymer. Further, because PVDF and PVA are crystalline polymers, we can orient the natural state.

SUMMARY OF THE INVENTION

[0007] The method for using piezo materials as programmable matter. Critical background information is that electrical pulses sent into the piezo material results in electrical signals coming out of the material. Often the output signals are of different frequency and certainly of different amplitude. We can exploit this for computation.

[0008] Certain materials, such as PVDF, PVA and PZT may be exploited for computation. Basically, blocks of these substrates become programmable matter. One key feature of the programmable matter introduced here is that each instance of it, each sample, can be configured to behave as a capacitor network at low

frequencies (for example below a threshold of 10kFlz using PZT), but quite similar to one another at higher frequencies. This suggests a new type of secure computing. Taking advantage of the unique behaviour below the threshold, first a low frequency challenge response is performed, and the individual device authenticated. Then a set of higher frequencies are sent to perform the computation. If the device fails the authentication test then the computation can be stopped.

[0009] Secure computing first suggests a physically unclonable function (PUF). These are devices for computer-user authentication. The most secure versions are called strong PUFs and involve multiple“challenge-response” pairs. A simpler version is as a lock dongle with the potential to immobilize a computer.

[0010] A strong PUF that is essentially unique, and programmable, suggests using the device for secure function evaluation after programming it (pinning the dipoles into place at specific frequencies) with a genetic algorithm. [0011] This also suggests several types of neural networks, reservoir computing architecture, including one-view content addressable memory, convolution computation chip, long/short term memory recurrent network and a more advanced device known as a transformatron (US Patent 6,735,336).

[0012] In accordance with an aspect of the invention there is provided an integrated circuit comprising a solid volume of piezoelectric material having a plurality of faces. The piezoelectric material comprises at least one input configured on a first face and arranged to input a first signal in the piezoelectric material; and at least one output configured on a second face of the piezoelectric material different to the first face and arranged to output a second signal from the piezoelectric material, the second signal having characteristics dependent at least in part on a

characteristic of the first signal and a molecular characteristic of the piezoelectric material. The molecular characteristic of the piezoelectric material may relate to a molecular dipole moment within the piezoelectric material. In this way, as the input signal interacts with the molecular dipole moment within the piezoelectric material, a second signal having characteristics different to the input signal is output from the piezoelectric material. In this way the piezoelectric material may be used as an integrated circuit (e.g. as a microchip).

[0013] The piezoelectric material may comprise any one of the polymers: a) Polyvinylidene fluoride (PVDF);

b) Polyvinyl alcohol (PVA);

c) Polyvinylidene fluoride blended with polymethylmethacrylate;

d) Polyvinyl alcohol blended with polymethylmethacrylate.

[0014] In certain embodiments the piezoelectric material may comprise

Lead zirconate titanate (PZT). [0015] The piezoelectric material may comprise one or more carbon nanotubes.

[0016] In certain embodiments the integrated circuit of may comprise: first and second electrodes arranged on respectively a third and fourth face of the piezoelectric material, and arranged to transmit an electric programming signal there between; and wherein the at least one output is arranged to output the second signal from the piezoelectric material, the second signal having characteristics dependent at least partly on the characteristic of the first signal, the molecular characteristic of the piezoelectric material, and a characteristic of the electric programming signal. Advantageously, the application of a programming signal across the piezoelectric material affects the molecular dipole moment of the material, which in turn affects the interaction of the first signal with the molecular dipole moment of the material. Since the characteristics of the output second signal are dependent on the interaction of the first signal with the molecular dipole moment, any variation of this interaction affects the characteristics of the output second signal. Accordingly, by varying the characteristics of the programming signal, it is possible to vary the characteristics of the output second signal for any given first input signal. This behavior is exploitable for increasing the versatility of the integrated circuit.

[0017] The programming signal may be modulated in dependence on a genetic algorithm.

[0018] In accordance with a further aspect of the invention there is provided a method of controlling access to an operatively coupled device using a solid volume of piezoelectric material, the solid volume of piezoelectric material having a characteristic threshold frequency below which it exhibits physical behavior unique to it. The method may comprise: receiving a first signal at a first face of the solid volume of piezoelectric material, the first signal having a frequency less than the threshold frequency; receiving a second signal output from a second face of the solid volume of piezoelectric material; enabling access to the operatively coupled device in dependence on the second signal having a characteristic substantially consistent with an expected signal. Inputting a signal having a frequency below a characteristic threshold frequency of the solid volume of piezoelectric material results in a second signal being output from the piezoelectric material having physical characteristics (e.g. frequency and/or amplitude) unique to the specific specimen of piezoelectric material. In this way it is possible to use the solid volume of

piezoelectric material as a security device. For example, the solid volume of piezoelectric material may be effectively used as a secure key to access a computer terminal, for example. In order to access the computer terminal, a user would need to be in possession of the specific solid volume of piezoelectric material. A different yet identically shaped solid volume of the same piezoelectric material, would not display identical behavioral characteristics, and would not output a second signal having the same characteristics as the genuine volume of piezoelectric material, due to slight differences in the crystalline structures of both volumes of piezoelectric material.

[0019] In certain embodiments the threshold frequency may be

approximately 10 kHz. In this way the unique behavior of the solid volume of piezoelectric material is displayed for input first signals having frequencies lower than 10 kHz. [0020] In certain embodiments the second signal may comprise one or more physical characteristics at least partly dependent from the frequency of the first signal and a molecular dipole moment within the solid volume of piezoelectric material.

[0021] In accordance with yet a further aspect of the invention, there is provided a method of using a solid volume of piezoelectric material having a plurality of faces as an integrated circuit. The solid volume of piezoelectric material may comprise at least one input and one output configured respectively on first and second faces of the piezoelectric material. First and second electrodes may be configured respectively on third and fourth faces of the piezoelectric material. The method may comprise: transmitting a programming signal between the first electrode and the second electrode; inputting a first signal in the piezoelectric material from the at least one input; outputting a second signal from the at least one output, the second signal having a characteristic dependent at least partly on a molecular characteristic of the piezoelectric material, and a characteristic of the programming signal.

[0022] The molecular characteristic of the piezoelectric material may comprise a molecular dipole moment within the piezoelectric material at least partly dependent on the characteristic of the programming signal.

[0023] The characteristic of the programming signal may comprise any one of:

a) an amplitude of the programming signal;

b) a frequency of the programming signal.

[0024] The method may comprise varying a characteristic of the

programming signal in order to vary the characteristic of the output second signal. [0025] The method may comprise modulating the programming signal using a genetic algorithm, in order to vary the characteristic of the output second signal.

[0026] Yet a further aspect of the invention relates to a method of reservoir computing, comprising: using the aforementioned integrated circuit to generate an output signal in dependence on an input signal.

Using Piezo Materials as Programmable Matter

[0027] This invention relates to using piezo materials as programmable matter. Critical background information is that electrical pulses sent into the piezo material results in electrical signals coming out of the material. Often the output signals are of different frequency and certainly of different amplitude. We can exploit this for computation.

[0028] Computation is a dynamical process. Wolfram (2002) has developed the principle of computational equivalence in which he shows that computation is simply a question of translating inputs and outputs from one system to another. It is important to remember that the computers on our desktops are extensively designed systems for deterministic input-output relations. It is we humans who have designed them with specific interpretations for the I/O relations. A CPU is essentially a glorified reconfigurable lookup table. With piezo materials as programmable matter we are interpreting the input/output relations as the

computation.

[0029] A simple example of an application of this would be for a physically unclonable function. We attach electrodes, say a 4 X 4 grid, to two opposite faces of a 2cm cube of the piezo material. We send and receive what are called challenge- response pairs for authentication. An input set of signals is sent into the piezo material and an output set is received. One then matches the output with the expected output to verify the security. Multiple challenge-response pairs may be sent for complex verification. Each piezo sample will potentially have a different set of challenge-response pairs.

[0030] To program the piezo material for more serious applications, consider the following. Given a 2-cm cube of the material with 4 X 4 grid of electrodes on all six faces. We assign one face (16 wires) as input and the opposite face (16 wires) as output. We then assign the 16 wires on each of the other four faces (64 wires total) as programing signals. When we send an input vector (16 pulses) to the input wires, we will observe 16 outputs on the output lines. Sending other pulses (up to 64) into the wires on the perpendicular faces will result in constructive and destructive interference of pulses between these programming and input signals. This will result in a modulation of the output signals. These input and programming signals with unique output signals constitute a computation. Wherein the computation is a result of the complex dynamics that takes place within the matrix.

[0031] One could use a genetic algorithm for the actual“discovery” of the program control signals. We’ll briefly describe the genetic algorithm for this application. Details are found in Goldberg (1989).

[0032] Imagine we want to find a mapping relation between one 16- element set of pulses (input) and another 16-element set of pulses (output). This could be, for example, a pattern matching task. To map the input to the output we dither with the programming signals (64 of them) until we have a good match between the input and output. The details of this dithering process are the genetic algorithm. The end result is we have a 64-element set of pulse signals that allow us to map the 16 inputs to the 16 outputs.

[0033] What makes this a secure computation is that when the power driving the signals is turned off, the dipoles in the piezo material relax. If we want to match a particular input pattern to a particular output pattern in which we have already developed the programming pattern (64 signals), we simply set the input and programming signals to the appropriate values and read out the answer on the output wires.

[0034] The matrix material is said to be programmable matter. When pulses of varying frequency are sent into the matrix they interact in complex ways and produce complex pulse streams at the outputs. The input pulses, say N time- streams, interact to produce M output pulse time-streams. There is a unique relationship between these, inputs and outputs, given by the relation M = f(N), where M and N are multi-dimensional vectors in time. When the power driving the input pulses is disconnected, the“stored” interacting pulse patterns dissipate. This immediately suggests three applications: 1 ) physical unclonable function (PUF), 2) reservoir computing neural network and, 3) a system that can“compute” complex secure functions that were previously programmed using a genetic algorithm.

Programming the Matrix with a Genetic Algorithm

[0035] Genetic algorithms (GA) are typically used for complex

optimizations (Goldberg, 1989). Suppose we want to program a FPGA to output a pulse train of 1 MHz, 50% duty cycle on pin number 9. An FPGA is a 2-dimensional array of logic blocks that may be arranged in an almost unlimited number of possibilities. The goal for the GA is to wire the insides of the FPGA so as to produce consistent 50% duty cycle, 0 to 5 volt pulses at 1 MHz. So, our fitness function, that is how we evaluate the goodness of the wiring of the insides of the FPGA, can be directly measured by digital pulse counting techniques.

[0036] The genetic algorithm begins now. We start with a population of, say 10-bit strings that represent all possible connections within the FPGA. We sequentially download each of the bit strings and evaluate the output at pin 9. This evaluation may result the following outputs for 10-bit strings: string 1 , nothing;

string 2, nothing; string 3, 1 Hz; string 4, nothing; string 5, 100 Hz; string 6, nothing; string 7, 10 Hz; string 8, nothing; string 9, nothing; string 10, 1 Hz. We now have a direct relationship between the bit strings, as circuits in the FPGA, and their performance as oscillators. The bit strings that resulted in some oscillations

(numbers, 3, 5, 7, 10), we will combine by simply swapping pieces of bit strings. This is called crossover. We will take the remaining bit strings and mutate them, i.e. we flip, say 50% of the bits. This completes the first generation. We repeat this for, say 1000 generations or until we get a good bit string producing the desired oscillations.

[0037] The operations we just described can also be done with the Matrix cube. Other machine learning applications where there is an obvious goodness metric e.g. pulse counting, are also possible. As an example, consider, image recognition where we have a measure of the goodness of fit of the image, from an initially sloppy preforming member of a population, and the final desired result.

[0038] Once the bit string representing the oscillation at pin 9 is

discovered, or the image recognition bit string has been found, we just apply this bit string to the matrix, send in the input string in the case of the image, and read out the results on the appropriate output pins. When the pulses representing the input and control bit strings are removed, the matrix relaxes to its initial state and there is no trace of what the matrix was used for.

Reservoir Computing with the Matrix

[0039] A reservoir computing system takes an input vector and“computes” an output vector. The“computation” is done by a randomly connected matrix of neurons. So, the computation is a vector matrix multiplication of the input and this random matrix. The output is a vector. Because we are not changing anything on the inside of the matrix, each input vector will map to a unique output vector. To now make use of this for, an image recognition task, for example, we take these output vectors and map them to a desired output by multiplying this output vector from the matrix-cube with another matrix of random weights in the digital computer controlling the system. We use a gradient descent to adjust these weights in the matrix in the computer to give the final output. Naturally we can change the random matrix, our cube, dynamically from control bits and this increases the usefulness of the matrix material as a reservoir computer.

PUF Computing with the Matrix

[0040] In one implementation the Matrix is a small unit that locks a computer. If it is not inserted the machine is disabled. While such devices exist today they can be hacked and cloned. Once the Matrix is removed from power it reverts to its normal state leaving no trace of the program or data that was stored on it.

[0041] In another implementation the Matrix is used to protect

unauthorized access to specific programs. [0042] At certain frequencies any cube of Matrix material behaves the same way as all others. However, at low frequencies (below 500 HZ) there is sufficient time between each pulse for the material to revert to its resting state. Under these circumstances every volume of the material is shown to be unique.

References

Transform atron (US Patent 6,735,336).

J. M. Hughes (2016),“Arduino: A Technical Reference, O’Reilly, Boston.

H. Yamagami and E. Fukada, (1973)“A Model Experiment for Piezoelectric

Relaxations in Polymers” Polymer Journal, vol 5. No. 3, 309-315

Goldberg, D. (1989)“Genetic Algorithms in Search, Optimization and Machine Learning” Addison Wesley, Reading, MA

Wolfram, S. (2002),“A New Kind of Science”, Wolfram Media, Champaign, IL

[0043] Various objects, features, aspects, and advantages of the present invention will become more apparent from the following detailed description of preferred embodiments of the invention, along with the accompanying drawings in which like numerals represent like components.

[0044] Moreover, the above objects and advantages of the invention are illustrative, and not exhaustive, of those that can be achieved by the invention. Thus, these and other objects and advantages of the invention will be apparent from the description herein, both as embodied herein and as modified in view of any variations which will be apparent to those skilled in the art.

BRIEF DESCRIPTION OF THE DRAWINGS [0045] One or more embodiments of the invention will now be described, by way of example only, with reference to the accompanying diagrams, in which:

[0046] Figure 1 shows an example of polymer crystallites, which can be thought of as rigid supramolecular structures separated by regions of higher mobility. [0047] Figure 2 shows a frequency-impedance plot for PVDF device shown in the inset photo. Essentially the molecular and supramolecular structure acts as tiny capacitors connected via other tiny capacitors resulting in a capacitor network.

[0048] Figure 3 shows a small capacitor array with frequency-impedance plot.

[0049] Figure 4 shows an example of an experimental embodiment of proposed invention. The photo shows embedded electrodes. Blue RTV glue has been attached as strain relief. The embedded electrodes are not actually visible. The photo was edited to show the embedded electrodes.

[0050] Figure 5 shows an impedance spikes at resonance frequencies associated with the applied 3V square wave.

[0051] Figure 6 shows an example of a simple computational system from a physics perspective. For any given input, there is a unique output.

[0052] Figure 7 shows a first prototype designed for evaluation of the

PVDF programmable matter. The photo on the left shows the circuit board just prior to silver epoxying the 2-mil film into place. The photo on the right shows the completed system ready to connect to an Arduino.

[0053] Figure 8 shows the x-axis is an arbitrary time scale and the y-axis is an arbitrary voltage scale. Things have been shifted to fit on same graph. Notice when the power is“on” the system oscillates at a particular frequency. Then when the power is off the oscillations continue at a different frequency and they begin to look like a damped oscillator. [0054] Figure 9 shows a small printed circuit board (PCB) to which a 3mm X 2cm X 2.5cm piece of PVDF was silver epoxied. Wires attached to the indicated locations. A second PCB was epoxied to the other large face of the PVDF.

[0055] Figure 10 shows a prototype PVDF programmable matter system. See text for details.

[0056] Figure 11 a shows one nibble 8-bit modulo counter circuit on the left and two of them constructed on a breadboard shown in the photo on the right. These 8-bits acted as inputs to the PVDF matrix. Figure 11 b is a schematic circuit diagram of the circuit shown in Figure 11 a.

[0057] Figure 12a shows an output display consisting of 8 LM3914 chips and bar-graph display LEDs. Figure 12b is a schematic circuit diagram of the circuit shown in Figure 12a.

[0058] Figure 13 shows a Ten Matrix Cube of PZT which can be configured in any pattern. The illustrated example shows a cube having four inputs and four outputs.

[0059] Figure 14 shows a Ten Matrix Cube device shown in Fig 13. Here the results for - four inputs (A02- A05) four outputs (A08 -A11 ) are shown.

[0060] Figure 15 shows four cubes of Matrix material each have five electrodes attached, one to each of their five faces. Four of the electrodes are then connected to oscillators which send pulses at 1 , 2, 3 and 10 HZ respectively.

[0061] Figure 16 shows four cubes of Matrix material each have five electrodes attached, one to each of their five faces. Four of the electrodes are then connected to oscillators which send pulses at 100 kHz, 200 kHz, 500 kHz and 1 MHz respectively DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0062] Before the present invention is described in further detail, it is to be understood that the invention is not limited to the particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0063] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0064] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, a limited number of the exemplary methods and materials are described herein.

[0065] It must be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. [0066] All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates, which may need to be independently confirmed.

[0067] By applying a high voltage potential, we can amplify the dipole moment in PVDF, or PZT by moving the dipoles to the positive or negative side of the applied electric field. Then when the voltage potential is turned off, we find that the dipoles are essentially frozen into place. This is the conventional approach to “polling” PVDF or PZT for its sonic and ultrasonic applications.

[0068] Focusing just on the polymer system, PVDF, defects in the packing arrangement of the crystallites can also affect the dipole moment. So, the fact that native PVDF has a high dipole moment is by itself, not important; however, the fact that PVDF forms crystallites with potential defects allows this material to have an effective higher dipole moment, by external electric or acoustic manipulation.

[0069] Figure 1 shows a schematic of polymer crystallites. The tiny crystal regions are essentially supramolecular structures separated by tangled polymer regions. These tangled regions have a higher mobility than the crystal regions.

Though we have no nano-scale imaging, it is easy to imagine that in an electric field these crystallites align. With a square pulse of 5V and > 100Flz frequency, the relaxation time of these crystallites is measured to be about 400 microseconds. This means, in even small fields of a few volts the crystallites will align and relax. This dipole modulation changes the dielectric constant over the length scale of the attached electrodes. This change in the dielectric constant is a key parameter of capacitors.

[0070] Figure 2 shows a frequency-impedance plot from a 2cm rectangular block of PVDF. The figure also shows an inset photo of the device. The four embedded electrodes were used with an HP 4192A, four-probe, impedance analyzer. The impedance plot is essentially the result for the inherent molecular capacitor network

[0071] Further, enhancements and electronic properties of the PVDF matrix may be obtained by blending with polymethylmethacrylate (PMMA). This allows the mobile dipoles greater freedom. Lastly, by blending carbon nanotubes with PVDF we can essentially make a molecular-scale resistor-capacitor network which may be used for computation - programmable matter.

[0072] The molecular-scale dynamics inside these materials can be manipulated with electronic signals. Capacitors can essentially be thought of as frequency dependent resistors. This is clearly seen in Figure 3 for a simple capacitor network comprised of four 100 mF and four 120 mmF capacitors all in series. The total capacitance for this array is given by:

[0073] In capacitors and networks thereof, the imped |Z| goes down as the frequency goes up. On a log-log scale this is a linear relation.

[0074] In this patent disclosure, we describe how to modulate, or control, the impedance of a material that acts as a capacitor network. On the molecular scale, many materials have dipoles that can be manipulated in acoustic or electric fields. In the case of polymers with crystallites, we can move the crystallites over nanometer scales and thus change the overall impedance of a sample of the polymer.

[0075] Figure 4 shows a photo of a simple rectangular block of PVDF with four electrodes embedded and glued into place with conductive silver epoxy. Two faces also have silver epoxy electrodes. Thus, we have four electrodes for an impedance measurement, and two electrodes for sending an electronic pulse signal.

[0076] Connecting the four embedded electrodes to an HP 4192A impedance analyzer and connecting the two face electrodes to a function generator (signal and ground) allowed us to measure the effects of on-going, 3V square wave pulse. The results of this are shown in Figure 5.

[0077] These results show proof of concept. The concept could be extended. A grid array of electrodes may be placed on two faces of, for example, a cube of PVDF. Two other faces on the PVDF cube may have a gird of silver, gold, or nickel electrodes for electronic control and electronic signal input/output. When the system is powered up, modulations in the dipole-density will induce an analog electronic circuit.

[0078] This concept could be extended. A grid array of transducers may be placed on four adjacent faces of, for example, a cube of PVDF. Two other, parallel faces on the PVDF cube may have a gird of silver, gold, or nickel electrodes for electronic control and electronic signal input/output. When the transducers are powered up, modulations in the dipole-density will induce an analog electronic circuit. It may be necessary to pin some dipoles in place with added electronic signals at the“pinning frequency.” This pinning frequency will depend on the type of material.

[0079] A system like this could be programmed with a genetic algorithm to compute a specific function. This special function would simply disappear when the power to the transducers is turned off. This could prove to have important security applications.

EXAMPLES

[0080] From a simple physics perspective computation is an input-output relation (Figure 6). For some given input signal, a unique output signal is produced. To demonstrate the programmability of a sample of PVDF (programmable matter) several prototype devices were constructed. One of our first prototypes was a thin film 2 mil thick spread across some electrodes and glued into place with silver epoxy.

[0081] Figure 7 shows a photo of the device while under construction and after construction. It was designed to be interfaced to an Arduino programmable I/O board (Flughes, 2016).

[0082] The first results with this system are shown in Figure 8. A constant on-off signal to pin 7 resulted in sustained oscillations when off and when on. But both sets of oscillations had a different frequency. The amplitude of the signals observed on pins 1 -6 and pin 8 varied but were, for the most part, in phase.

[0083] These surprising results suggest an application in frequency multiplication.

[0084] In another experiment, we used a 3mm piece of PVDF measuring 2cm X 2.5cm. This was attached with silver epoxy to a small circuit board with some of the pads etched away with a Dermal tool. Figure 9 shows the circuit board and the pins connected thereto. There were thus 10 connections to each of the two largest faces on the PVDF sample. With two such printed circuit boards there were a total of 20 connections.

[0085] Our goal with this prototype, was to explore in a semi-automated way the range of possible behaviors. First, from initial experiments with a function generator sending 5Vpp pulses into the device on one side we could read out pulses on the other side that had amplitudes between mV and 2V. The polycrystal of PVDF and its small PCBs was mounted on a circuit board with ribbon cable and terminals for input/output.

[0086] Figure 10 shows a photo of the“packaged” system. We built a system comprised of two nibbles (one byte), each was controlled independently through separate clock or pulse generator.

[0087] The output from the modulo 8 counters was sent to LEDs for visualization and to AND gates. The other logical input to the AND gates came from third clock and is one of the inputs at the screw terminals shown in Figure 12.

[0088] One of the two modulo 8 counters is shown in Figures 11 a and 11 b.

The electronics is straight-forward, and we make no new claims about originality of the modulo 8 counters. On the right of the figure is a photo of the circuit. The ribbon cable from the prototype system is clearly seen.

[0089] Since we knew the output varied by only a few tenths of volts, and was essentially an analog signal, we decided to use an LM3914 bar-graph display chip. Again, we claim no originality is this circuit. It was selected as standard low- voltage signal visualization and compatible with CMOS. Figure 15 shows the schematic for one of the LM3914 circuits. Eight of these were constructed; one for each of the eight outputs. The final circuit is show in the photo of Figure 12a and schematically represented in Figure 12b. [0090] Monitoring all the outputs on oscilloscopes we see, as expected, complex repetitive patterns showing that unique inputs give unique outputs and the same unique input will give the same unique output (see Figure 5).

Experiments with PZT as Programmable Matter

[0091] In the prototype shown in Figure 13, ten matrix cubes are

connected to form an array with four inputs and four outputs. The remaining connections are between different cubes. When we put N inputs into the system and obtain M outputs, the M outputs are a "composition" of the N inputs.

Matrix Experiments 11/2017

[0092] In order to discover the frequency range for randomness and reproducibility we did further experiments at a range of frequencies. These

experiments conducted in late November 2017 demonstrate that above 1 MFIz the Matrix achieves repeatability. They also show that the behavior of similar blocks of Matrix material produce similar results. The consistent behavior strengthens its potential use for reservoir computing and genetic algorithms.

[0093] In one representative experiment shown in Figure 15, four cubes of Matrix material each have five electrodes attached, one to each of their five faces. Four of the electrodes are then connected to oscillators which send pulses at 1 , 2, 3 and 10 HZ respectively.

[0094] There is no direct contact between input and output. A

representative set of the input signals are shown at A02 - A05. The output signals for each cube are shown at A10 - A13.

[0095] There is no consistent pattern between the four, thus demonstrating that at low frequencies, there is sufficient time for the matter to rest before the next pulse arrives. In consequence we can see the unique nature of each volume of the material.

[0096] In another representative experiment shown in Figure 16, four cubes of Matrix material each have five electrodes attached, one to each of their five faces. Four of the electrodes are then connected to oscillators which send pulses at 100 kHz, 200 kHz, 1 MFIz and 500 khlz respectively.

[0097] There is no direct contact between input and output. A

representative set of the input signals are shown at A02- A05. The output signals for each cube are shown at A10 - A13.

[0098] There is a closely correlated pattern between the four, thus demonstrating that at high frequencies, there is insufficient time for the matter to rest before the next pulse arrives. In consequence we can see a replicable pattern.

Therefore, it is claimed that it is possible to mass produce Matrix cubes and account for minor differences in individual volumes.

[0099] We can say that the Matrix array is computing the function M = f(N) where M and N are time-stream vectors.

Matrix Design Circuit Schematic

[0100] The techniques, processes and methods described may be utilized to control operation of any device and conserve use of resources based on conditions detected or applicable to the device.

[0101] The invention is described in detail with respect to preferred embodiments, and it will now be apparent from the foregoing to those skilled in the art that changes and modifications may be made without departing from the invention in its broader aspects, and the invention, therefore, as defined in the claims, is intended to cover all such changes and modifications that fall within the true spirit of the invention.

[0102] Thus, specific methods for secure programmable matter have been disclosed. It should be apparent, however, to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the disclosure. Moreover, in interpreting the disclosure, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms "comprises" and "comprising" should be interpreted as referring to elements, components, or steps in a non- exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced.

Claims

1. An integrated circuit comprising:

a solid volume of piezoelectric material having a plurality of faces;

at least one input configured on a first face and arranged to input a first signal in the piezoelectric material; and

at least one output configured on a second face of the piezoelectric material different to the first face and arranged to output a second signal from the

piezoelectric material, the second signal having characteristics dependent at least in part on a characteristic of the first signal and a molecular characteristic of the piezoelectric material.

2. The integrated circuit of claim 1 , wherein the molecular characteristic of the piezoelectric material comprises a molecular dipole moment within the

piezoelectric material.

3. The integrated circuit of claim 1 or 2, wherein the piezoelectric material comprises any one of the polymers:

a) Polyvinylidene fluoride (PVDF);

b) Polyvinyl alcohol (PVA);

c) Polyvinylidene fluoride blended with polymethylmethacrylate; d) Polyvinyl alcohol blended with polymethylmethacrylate.

4. The integrated circuit of claim 1 or 2, wherein the piezoelectric material comprises Lead zirconate titanate (PZT).

5. The integrated circuit of claim 3, wherein the piezoelectric material comprises one or more carbon nanotubes.

6. The integrated circuit of any preceding claim, comprising: first and second electrodes arranged on respectively a third and fourth face of the piezoelectric material, and arranged to transmit an electric programming signal there between; and wherein

the at least one output is arranged to output the second signal from the piezoelectric material, the second signal having characteristics dependent at least partly on the characteristic of the first signal, the molecular characteristic of the piezoelectric material, and a characteristic of the electric programming signal.

7. The integrated circuit of claim 6, wherein the programming signal is modulated in dependence on a genetic algorithm.

8. A method of controlling access to an operatively coupled device using a solid volume of piezoelectric material, the solid volume of piezoelectric material having a characteristic threshold frequency below which it exhibits physical behavior unique to it, and the method comprises:

receiving a first signal at a first face of the solid volume of piezoelectric material, the first signal having a frequency less than the threshold frequency;

receiving a second signal output from a second face of the solid volume of piezoelectric material;

enabling access to the operatively coupled device in dependence on the second signal having a characteristic substantially consistent with an expected signal.

9. The method of claim 8, wherein the piezoelectric material comprises any one of the polymers:

a) Polyvinylidene fluoride (PVDF);

b) Polyvinyl alcohol (PVA);

10. The method of claim 8, wherein the piezoelectric material comprises Lead zirconate titanate (PZT).

1 1 . The method of claim 9 or 10, wherein the threshold frequency is approximately 10 kHz.

12. The method of any one of claims 9 to 1 1 , wherein the second signal comprises one or more physical characteristics at least partly dependent from the frequency of the first signal and a molecular dipole moment within the solid volume of piezoelectric material.

13. A method of using a solid volume of piezoelectric material having a plurality of faces as an integrated circuit, the solid volume of piezoelectric material comprising at least one input and one output configured respectively on first and second faces of the piezoelectric material, and first and second electrodes configured respectively on third and fourth faces of the piezoelectric material, the method comprising:

transmitting a programming signal between the first electrode and the second electrode;

inputting a first signal in the piezoelectric material from the at least one input; outputting a second signal from the at least one output, the second signal having a characteristic dependent at least partly on a molecular characteristic of the piezoelectric material, and a characteristic of the programming signal.

14. The method of claim 13, wherein the molecular characteristic of the piezoelectric material comprises a molecular dipole moment within the piezoelectric material at least partly dependent on the characteristic of the programming signal.

15. The method of claim 13 or 14, wherein the characteristic of the programming signal comprises any one of:

a) an amplitude of the programming signal;

b) a frequency of the programming signal.

16. The method of any one of claims 13 to 15, comprising:

varying a characteristic of the programing signal in order to vary the characteristic of the output second signal.

17. The method of any one of claims 13 to 16, comprising:

modulating the programming signal using a genetic algorithm, in order to vary the characteristic of the output second signal.

18. A method of reservoir computing, comprising:

using the integrated circuit of any one of claims 1 to 7 to generate an output signal in dependence on an input signal.