Authentication and / or identification method based on an artificial engraved fingerprint

Abstract

A computer implemented authentication and / or identification method for an authenticable object having an engraved surface defining an artificial fingerprint manufactured through a mask originated by block-copolymers, comprising the steps of capturing, e.g. via an atomic force microscopy or scanning electron microscope the artificial fingerprint and elaborating via a binarization algorithm to generate a check image of the artificial fingerprint; and comparing data based, on the check image and data based on a binarized fingerprint master image of an authentic fingerprint to define whether the authenticable object is authentic and / or to identify an object.

Description

[0001] Authentication and / or identification method based on an artificial engraved fingerprint originated by a mask from block copolymers

[0002] FIELD OF INVETION

[0003] The present invention refers to an authentication and / or authentication method based on physical unclonable functions (PUFs) originated through masking by self-assembled block-copolymer templating, which are characterized by inherent stochasticity. By authentication is meant to verify the identity or another unique feature such as a serial number of a person or a good. In the case of a person, the PUF is associated to an object e.g. a card and the identity information of the person are associated in a database to the PUF engraved in the card.

[0004] PRIOR ART

[0005] PUFs are known wherein detection requires elaboration of an electric signal and precision of detection is improvable. Furthermore, it is known to generate the PUF and, in a subsequent step, apply the PUF on an object to be authenticated. This causes, at the nanoscale of the PUF, a certain chance of damage that negatively impacts on the authentication method. Furthermore, known techniques including PUF generation and attaching the PUF on the object or good are such that the PUF may potentially be removed or detached through appropriate chemical / physical treatments.

[0006] Authentication methods based on PUFs are generally known, in particular anticounterfeiting authentication methods.

[0007] Summary and scope of the invention

[0008] A scope of the present invention is to overcome at least in part the above-mentioned drawbacks.

[0009] The invention relies on a preparation step of generation of a mask and engraving on an object or good of an artificial fingerprint based on the mask by i) molecular self-assembly of block copolymers (BCPs) on a target substrate comprising an engravable material i.e. a hard material such as a solid metal or metal alloy and a ceramic material, ii) selective removal of one of the BCP phase in order to generate a mask, and iii) mask pattern transferring by engraving to the target substrate via the fingerprint-like geometry of the mask. By fingerprint is meant a 2D pattern having unclonable features in view of the randomness of such features. Figures show an embodiment of lamellae fingerprint resembling the pattern of a human fingerprint.

[0010] Once the fingerprint is engraved on the object or good and the engraved fingerprint is detected at a nanoscale via a suitable device such as a Scanning Electron Microscope (SEM) or an Atomic Force Microscope (ATM) or a nanoscale profilometer or the like capable of scanning by contact of a probe or by control of an energy beam at the nanoscale so as to generate a raw image of the fingerprint.

[0011] Such raw image, captured after engraving the fingerprint, is subsequently elaborated by a fingerprint enhancement algorithm able to adaptively improve the clarity of ridge and valley structures based on estimated local ridge orientation and frequency (Figure 2a) e.g. a tool available in Python. The output of the enhancement algorithm is considered the master and original image and this will enable subsequent authentication of the object on which the fingerprint is engraved. Preferably, the master image is stored in a database and associated to one or more additional feature of the object or good such as production parameters, material, shape, other identification codes such as QR codes, bar codes RFID, etc.

[0012] According to a first embodiment, the authentication is performed via a homographic computer vision algorithm, which operates by superimposing a master and a test enhanced fingerprint image in order to identify matching key points and estimate whether the test image is identical or not to the master image.

[0013] According to a second embodiment that provides a reach amount of data about the master raw image, via an imaging algorithm it is possible to extract a master binary code matrix to be stored in a database where additional data of the object or good are included e.g. those already cited above, in order to provide a set of authentication data of the object or good.

[0014] During an authentication process executed once or more times during the life of the object or good, the engraved fingerprint is detected e.g. via a scanning electron microscope or an atomic force microscope or the like, and with the same imaging algorithm used to extract authentication data from the fingerprint, a check binary code matrix is generated. In order to authenticate the object or good, the check binary matrix is compared to the master binary matrix.

[0015] As a matter of fact, it cannot be excluded a priori that a nanopattern can be cloned by using advanced and expensive lithographic tools with resolution < 10 nm, however line-edge roughness, line-width roughness and the 3D morphology of systems are unlikely to be reproducible, thus strengthening the proposed approach. The proposed artificial fingerprint is directly engraved on the target substrate and, thus, cannot be removed (unless the substrate is mechanically scratched, but this will leave marks). As an important consequence, the PUF robustness and environmental stability relies on the physical / chemical / mechanical properties of the engraved substrate. This means that tailored choice of the substrate would enable the realization of PUF devices working even when exposed to harsh conditions (high / low temperatures, exposure to chemicals, mechanical agents, radiations) as required, for example, for space applications.

[0016] Preferably, the artificial fingerprint is engraved and monolayer i.e. there is no added material during the generation of the artificial fingerprint the latter being the result of cut-outs from the surface of the object or good to be authenticated. Cut-outs are preferably engraved via etching or another process engraving a hard material at a nanoscale. The hard material may be a ceramic material, semiconductor material, dielectric or a metallic material. Brief description of the drawings

[0017] The invention will be described herein according to non-limiting embodiments and examples represented in the appended images, wherein:- Fig. 1 is a sketch of the main steps to obtain a nanoscale mask for an engraved artificial lamellae fingerprint according to the present invention;

[0018] - Fig. 2 is a sketch of the main steps from a raw scanned image of the engraved lamellae fingerprint to the identification of defects, according to an embodiment of the present invention;

[0019] - Figg. 3a and 3b shows the steps from the identification of defects to generation of a binary code matrix based on such defects; Figg. 3c and 3d show that bit uniformity and entropy of the binary code matrix are such to provide a reliable PUF;

[0020] - Figg. 4a and 4b show respective sketches of use for the artificial fingerprint of figure 1 ;

[0021] - Figg. 5 show diagrams about performance of a computer vision algorithm to recognize whether a test image is a check image previously stored.

[0022] Detailed description of the invention

[0023] A preparation step of the present invention comprises molecular self-assembly, which relies on phase separation of chemically distinct and thermodynamically incompatible polymeric components of BCPs that occurs randomly under thermal fluctuations, resulting, under proper conditions, in lamellar fingerprint-like patterns. BCPs are constituted by two or more different and chemically incompatible homopolymer chains (one hydrophilic and one hydrophobic) that are covalently linked together. Due to the amphiphilic nature of BCPs, even small structural differences of the constituent blocks determine an increase of the free energy resulting in a microphase separation under specific annealing conditions. However, unlike homopolymer blends, the covalent bond linking the two different blocks counterbalances the thermodynamic forces involved in the phase separation. This leads therefore to the in-parallel self-registration of periodic nanostructures in the range of 10 - 100 nm, that is the so-called self-assembly. The main factors that influence the self-assembly process are the polymerization degree (N), which represents the number of monomeric units in a polymer; Flory-Huggins interaction parameter (x), that describes the excess of free energy of mixing and estimates the immiscibility of the constituent blocks; volume fractions (fα and fβ) of each homopolymer. Changes in volume fraction affect the morphology and packing symmetry of the resulting phase-separated nanostructures. BCPs with two or more constituent blocks can self-assemble into four thermodynamically stable phases. Highly asymmetric BCPs with a ratio above 80:20 self-organize in zero-dimensional spheres arranged in a body-centered cubic (BCC) lattice of polymer B embedded in a polymer A matrix. Hexagonally packed (HP) cylinders of the minority component are formed for BCPs with ratio 70:30. Double gyroids are formed for a very narrow compositional ratio interval and two- dimensional lamellae are formed by highly symmetric (ratio 50:50) BCPs. On the other hand, the periodicity (L0) and the typical dimensions of the resulting nanostructures is determined by the total molecular weight (Mn), given by the multiplication of N with the molecular weight of the monomeric units (M0) of the BCP.

[0024] After achieving the self-assembled pattern, one of the two polymers comprising the BCP is selectively removed, resulting in the formation of a disordered nanolithographic mask resembling an artificial fingerprint. Figure 1 reports an example of self-assembled lamellar BCP showing fingerprint-like global features. It is important to stress that BCP are applied on the object or good by spin-coating and this deposition technique is flexible and can be used on a wide number of materials.

[0025] According to an embodiment, the surface of different materials (silicon, silicon oxide, quartz, diamond) is functionalized by RCP grafting process to promote the perpendicular orientation of lamellar BCP nanostructures. First, the substrates are cleaned in an ultrasonic bath in acetone followed by isopropyl alcohol, and functionalized by 02 plasma treatment at 130 W for 20 min. Then, a solution of RCP (18 mg in 2 ml of toluene) is spincasted for 60 s at 3000 rpm onto the functionalized substrates. The grafting process is performed in a rapid thermal processing (RTP) machine Jipelec JetFirst200 at high temperature (Ta = 290 °C) for an annealing time (ta) of 300 s, in a N2 environment with a heating rate of 15 °C s-1. Automatic cooling to room temperature is set to 240 s. The non-grafted polymeric chains are then removed by sonication in toluene for 6 min, resulting in a final grafted RCP layer thickness of ~7 nm, as measured by spectroscopic ellipsometry (alpha-SE ellipsometer from J. A. Wollam Co.). BCP solution (18 mg in 2 ml of toluene) is then spincasted over the RCP functionalized surface at 3000 rpm for 60 s resulting in a total BCP thickness of 35 nm. The self-assembly is promoted by RTP at 230 °C for 600 s in a N2 environment with a heating ramp of 15 °C s-1 and automatic cooling to room temperature for 240 s. Selective removal of the PMMA phase of self-assembled BCP is achieved by exposure of the samples to ultraviolet radiation (5 mW cm-2, λ = 253.7 nm) for 180 s followed by isotropic 02 plasma etching (40 W for 30 s).

[0026] The synthesis of a-hydroxy co-Br polystyrene-stat-polymethyl methacrylate (PS-stat-PMMA) random copolymer (RCP) with Mw = 14.60 kg mol-1, styrene fraction (fPS) of 0.59 and polydispersity index (PDI) of 1.30, is known. Lamellar-forming polystyrene-block-polymethyl methacrylate (PS-b-PMMA) BCP with Mw = 66 kg mol-1, fPS = 0.50 and PDI = 1.09, is purchased from Polymer Source Inc. and used without further purification. Toluene (99.8% anhydrous), acetone (99.5% anhydrous) and isopropyl alcohol (98% anhydrous) is purchased from Sigma Aldrich. Polymethyl methacrylate (PMMA) A4 positive electron-beam resist is purchased from MicroChem. Methyl isobutyl ketone (MIBK) developer was purchased from KemLab. According to an embodiment engraving artificial nano fingerprints on target substrates and materials is performed as follows.

[0027] The pattern transfer of the fingerprint pattern mask onto the substrate surface is performed by reactive ion etching (RIE) with different chemistries depending on the etched material. Any other chemical or chemical-physical method for surface matter removal with a pattern mask can be used. Silicon etching is achieved by SF6 / C4F8 RIE with ICP plasma of 750 W and RF table of 35 W. Silicon oxide and quartz etching is achieved by CHF3 / Ar RIE and power of 35 W. Diamond etching is achieved by 02 IIE with ICP plasma of 2000 W and RF table of 200 W.

[0028] After engraving by etching of the artificial fingerprint is completed, patterns are characterized by the presence of spatially distributed local features (defect points) closely resembling minutiae points of human fingerprints. In case of lamellar patterns, the defect taxonomy of both positive and negative phases includes terminal points, 3 -way junctions and dots. It is worth mentioning that typical features of lamellar structures such as typical dimensions, periodicity, correlation length, defect density and line-edge roughness can be tailored by appropriate choice of BCP molecular weights and processing conditions.

[0029] Acquisition of the engraved artificial fingerprint is preferably operated by Scanning Electron Microscope (SEM) or Atomic Force Microscopy (AFM) or any other device capable of detecting the engraved nanoscale artificial fingerprint. For example, the micrographs of the artificial fingerprint are acquired in a first scan by FEI Inspect-F field emission gun scanning electron microscope (FEG-SEM) using an Everhart- Thornley secondary electron detector (ETD). This technique can be used to detect the master image i.e. the image that shall be considered as the authentic one and thus associated to other authentic features of the authentic object. For detections intended to check authenticity, a dual-beam SEM FEI Quanta 3D FEG is employed, enabling the collection of the SEM micrographs through alternative instrumentation.

[0030] As for human fingerprints, a critical step for exploiting nanoscale fingerprints as PUFs is represented by the automatic extraction of the fingerprint features including minutiae (defects) from input images. This process is crucial and may heavily rely on the quality of the input fingerprint image. Since ridge structures in poor-quality fingerprint images can be not well defined, the pattern can be not correctly detected, and spurious defects can be created while genuine defects can be ignored. To increase the robustness of pattern extraction with respect to the quality of input fingerprint images, we adopted a known fingerprint enhancement algorithm able to adaptively improve the clarity of ridge and valley structures based on estimated local ridge orientation and frequency (Figure 2a) e.g. a tool available in Python . This allows to obtain a binarized fingerprint pattern that can be then exploited as input for automated analysis, via a known algorithm such as Automated Defect and Correlation Length Analysis, of the fingerprint based on BCP morphological parameters including defect localization (defect maps), line period, line width, lineedge roughness, line- width roughness, and correlation length (correlation maps). The process flow, from SEM / AFM image to fingerprint pattern enhancement and defect localization, is reported in Figure 2.

[0031] A comparison of a binarized pattern obtained by fingerprint enhancement algorithm and other common binarization thresholding techniques on the same micrograph is reported in Figure 2c. Fingerprint pattern enhancement outperforms traditional thresholding techniques, allowing to obtain a binarized map with reduced noise and with a reduced number of artifacts. Notably, whereas the effectiveness of traditional thresholding techniques relies on the image quality, contrast and brightness, fingerprint enhancement enables the automatized extraction of patterns even in case of poor quality and low contrast images. Moreover, it eliminates the need for imagedependent adjustment of binarization parameters, thereby avoiding any potential user bias. The reduced number of artifacts in images processed with fingerprint enhancement results in a limited number of spurious defects. Also, this approach enables the generation of correlation maps with a decreased noise level. A statistical analysis on 200 SEM micrographs, acquired on different areas of the same patterned sample, allows a quantitative evaluation on the effect of the binarization extraction of defects and correlation length of the structures. Fingerprint enhancement results in a lower mean defect density obtained over different images, quantitatively showing the possibility of reducing the counting of spurious defects through this binarization technique. Furthermore, larger defect distributions obtained by conventional binarization techniques are due to overestimation of defects in images not properly binarized through these techniques. More in detail, conventional binarization techniques result in a higher overestimation of certain types of defects. Since defect density is inversely related to the dimensions of lamellae orientation domains, fingerprint enhanced images result in a higher mean value of correlation length. All these findings show that binarization through the fingerprint enhancement automated algorithm allows to retrieve a genuine fingerprint pattern that, besides reducing artifacts, is user independent and can automatically extract relevant information even from poor-quality images and images acquired with different contrast / brightness conditions. These are fundamental aspects for correct authentication and identification of PUFs based on BCP patterned substrates in real-world scenarios.

[0032] Enhancement comprises in general the following steps: normalization; orientation image estimation; frequency image estimation; region mask generation; filtering. Greater details can be found in Hong, Lin, Yifei Wan, and Anil Jain. "Fingerprint image enhancement: algorithm and performance evaluation." IEEE transactions on pattern analysis and machine intelligence 20, no. 8 (1998): 777-789.

[0033] Binary code matrices encoding of artificial fingerprints

[0034] These artificial fingerprints can be considered as PUF where the nanopattern represents the unique response r when the surface is scanned with a focused beam of electrons representing the input challenge c. In this context, the uniqueness of the PUF response r=ƒ(c) is guaranteed by the intrinsic randomness of the self-assembly process of pattern formation, thereby relying on the internal and uncontrollable manufacturing variability of the system to establish the unique input / output relation f(·) . A first approach to exploit nanopatterns as physical unclonable functions is to generate a cryptographic binary code response of the system based on local features of nanoscale morphologies. In case of BCP templated patterns, the nano fingerprint feature can be converted to a binary code matrix by defining each pixel of the matrix as 1 -bit or O-bit depending on the presence or absence of minutiae in the corresponding spatial location, respectively, as reported in Figure 3a. According to an alternative, the binary code map can be built based on the defect map. This allows the realization of the corresponding binary code matrix reported in Figure 3b. Notably, the choice of the code matrix pixel size in these systems depends on morphological properties of the pattern. This means that, given a pattern area, the pixel size should be selected to ensure the maximum randomness and uniqueness of encoded binary matrices. Figure 3c reports the bit uniformity and fractional hamming distance (HD) (i.e., the percentage of bits that differs between two binary code matrices) between different binary code matrices as a function of the pixel size calculated by considering 200 images with an area of 2.76 x 3.20 μm2of different nanopatterns. As can be observed, the mean value of fractional HD of around 0.5 (meaning that paterns can be distinguished) is achieved in correspondence of a pixel size of 238 nm when a bit uniformity of around 0.5 can be observed (i.e., when different binary code matrices have almost equal numbers of 0 and 1) (the relationship between fractional HD in between patterns and bit uniformity of patterns is reported in Figure 3d). This is because bit uniformity is beneficial to achieve the maximum randomness, as testified by the maximum patern entropy that is found to be very close to the ideal value of 1 when bit uniformity is close to 0.5. Notably, a bit uniformity (and fractional HD) of 0.5 can be observed when the pixel size is approximately the average correlation length of BCP patterns (ξ= 206 ± 18 nm. It turns out that through appropriate selection of the pixel size of the binary code matrix (in this case 238 nm), it is possible to obtain binary code matrices with bit uniformity of 0.51 ± 0.06, unit entropy close to the ideal value of 1, and to obtain a distribution of fractional inter-HD between different binary code matrices with mean value of 0.51 ± 0.06. These results confirm the high randomness of the binarized code matrix obtained from local nanopattern features. In general, pixel size is selected so that bit uniformity of the binary code matrix is between 0.4-0.6 including uncertainty of estimation.

[0035] In this context, the encoding capacity of the system cp, defined as the number of possible responses exhibited by a random patern where c is the number of responses for each pixel while p is the image area in pixels, is inherently related to the morphological properties of the nanopattern. In this framework, the encoding capacity of the system can be improved through two strategies: i) by increasing the image area (i.e., increasing the number of pixels without changing the pixel size) and / or ii) by reducing the correlation length of the BCP patern to reduce the pixel size of the binarized code matrices while maintaining bit uniformity and fractional inter-HD of ~ 0.5). Interestingly, an encoding capacity of the same order of magnitude of the world population can be achieved by considering an image with area below 2 μm2while an encoding capacity larger than the number of atoms in the known universe can be achieved by considering an image area larger than 12 μm2, showing the high density of encoding capacity of the system. Concerning the second strategy that aims to increase the encoding capacity of the system for unit area, it can be achieved by proper selection of molecular weight of involved polymers and processing conditions to increase the defect density while reducing the correlation length of the system. Furthermore, it is worth mentioning that authentication protocols based on cryptographic codes obtained from fingerprint patterns PUFs can be further refined by considering not only the presence of defects in the pixel area but also defect density and / or defect types.

[0036] The comparison of a binarized code matrix with a database matrix, as required for authentication / identification, implies no misalignment of the image acquired by the end user with the corresponding image stored in the database. Indeed, even small translations, rotations and / or deformations of the end user image can cause incorrect authentication / identification due a different conversion of the fingerprint pattern to the binary code matrix. Indeed, this can cause bit flips in the related binary code matrix due to the different spatial localization of defects.

[0037] Computer-vision based authentication / identification approach

[0038] Robustness of the authentication / process in real-world scenarios can be enhanced by exploiting matching algorithms based on computer vision concepts. For this purpose, we synthesized 100 different PUF nanopatterns engraved on a SiO2substrate, and we built a database consisting of micrograph images of the corresponding fingerprint patterns acquired by a first scan. Then, micrograph images of the same PUF patterns were acquired in a second scan to obtain a set of images used for testing (test set). Authentication / identification were tested by comparing an image from the test set with a reference image from the database, where the genuine pattern is obtained through the fingerprint enhancement algorithm. For a given pair of previously binarized images, which we will refer to as reference and test image, respectively, we aim at determining whether they represent the same nanopattern. As expected in real-world scenarios, slight shifts and rotations are present between the database and the test set image of the same nanopattern. Given a reference / test pair of images, a set of matching keypoints between the two images are computed through a Scale-Invariant Feature Transform (SIFT) algorithm (other methods such as Speeded Up Robust Feature, SURF, algorithm and Oriented FAST and Rotated BRIEF, ORB, were tested and give similar results) and a FLANN (Fast Library for Approximate Nearest Neighbors)-based matcher. In general, it is possible to use any homographic transformation for overlapping the test image on the database image. Then we select a few points providing the best matches (we tested between 10-15, not significantly affecting the results), and use those as a guide to build a homography transformation to map the test image on top of the reference one. Examples of feature matching and overlapping of images by considering two images of the same nanopattem and on two images of different nanopatterns are reported in Figure 4a and b, respectively. If the two images are taken from the same nanopattern and thus only differ by such a kind of deformation, the homographic transformation automatically corrects for these effects, so that the resulting image should overlap with the reference one. If on the other hand the two images are from unrelated samples, the transformation obtained from the initial matching algorithm will be unable to return an image with a reasonable overlap with the reference. Finally, the quantification of the superposition was performed by considering the fractional HD calculated on the whole binary image obtained by superimposing the negative of the test image on top of the database image. The matching algorithm has been implemented by exploiting computer vision algorithms implemented in OpenCV python library. As the first step, the image was cropped to remove the Au frame of the

[0039] PUF device that can interfere with the binarization process. Then, the homographic transformation required for overlapping the test image and the database image was calculated through SIFT (SURF or ORB were tested to give similar results) and a FLANN-based matcher. Then, quantification of the superposition was performed by calculating the fractional HD of the binary image obtained by superimposing the negative of the test image on the reference image. Given the sharp nature of the binarized images, we typically obtain very clean cancellations when a good match is found, consistently leading to a clear separation between good and bad matches. According a homographic approach, the master image and the test image are compared to one another without generating any binary code matrix.

[0040] It is verified by experiments and a fractional HD matrix obtained by comparing images from the test set with the database, that a fractional intra-HD matching score of about 0.15 ± 0.01 is assigned to images of the same PUF, while fractional inter-HD of about 0.55 ± 0.02 is assigned while comparing images of different PUFs. Notably, a clear separation of intra and fractional inter-HD distributions can be observed, further showing the robustness of the authentication / identification protocol.

[0041] In this context, it is worth noticing that the extraction of the binarized fingerprint pattern from the micrograph image through the fingerprint enhancement algorithm provides noise robustness to the authentication process, a crucial aspect for exploiting image- based PUFs for real- world applications. In this context, clear separation of fractional intra and inter-HD distributions can be observed also by considering a test set of images acquired by different users operating with different equipment, further corroborating the robustness of the authentication / identification protocol. An analysis of the stability and robustness shows that PUFs can be correctly authenticated / identified even after 6 months when the sample was left in normal ambient conditions, revealing long-term reliable operation. Furthermore, the high thermal stability of PUFs INRIM was demonstrated by exposing samples to the harsh conditions of 200 °C for 30 minutes where the PUF experienced a high heating rate of 15 °C s-1and a high cooling rate of 0.8 °C s-1. Also after annealing, fractional intra and inter-HD distributions after thermal treatment still show clear separation.

[0042] Figure 4b shows a possible business environment wherein a manufacturer of an authentic object or good, engraves the authentic objects to obtain a PUF, elaborates the raw image from AFM / SEM to extract the master binary code matrix based on the defects of the artificial fingerprint, and stores the master matrix with additional data of the authentic object in a database for later inspection e.g. by the end-user or downstream stakeholders of the supply chain.

[0043] According to a preferred embodiment, as BCP-templating is compatible with CMOS technology, one embodiment of the invention provides the integration of PUF devices mentioned above with conventional electronics. Note that the possibility of realizing fingerprints directly on chips to assess their legitimacy can be crucial to face back doors and hidden functionalities of fraudulent clone chips that, in the framework of global shortage of chips, can threaten global security. Figure 5a shows an example of a successful overlapping of two images of the same nanopattern through homographic transformation, where overlapping is evaluated through XOR operation. When images of the same nanopattern are compared, the XOR overlapped image is composed mainly of “0” values. Figure 5b is an example of a non-successful overlapping of two images of different nanopatterns, where the homographic transformation gives rise to an overlap with strong distortion of the test image with respect to the database image. In this case the XOR overlapped image is constituted of almost equal “0” and “1” values. In figures a and b the colored lines connecting the test image with the check image represent the matches of the homographic transformation, in fact they connect corresponding key points (represented by circles) in the two images. Dashed lines in the XOR overlapped images represent the area of image overlapping. Figures 5c-e show heat-map matrix representing the fractional HD between images from the test set and images from the database (c), images acquired after 6 months and images form the database to test aging stability (d), images acquired after thermal annealing of PUF devices at 200°C for 30 min and images form the database to test thermal stability (e), and Figures 5f-h. corresponding intra and inter distributions. Dashed lines in figures f-h represent the decision threshold evaluated from the comparison between the test set and the database.

[0044] In order to identify where the engraved fingerprint is located, the latter can be surrounded by a polygonal frame e.g. a square frame e.g. applied about the engraved fingerprint. For this purpose, the area of interest was defined by electron-beam lithography (EBL) in a dual-beam SEM FEI Quanta 3D FEG on PMMA A4 positive-tone resist on the patterned SiO2 substrate. After the development with MIBK:IPA (3: 1) developer solution, a double layer of Ti / Au (2 nm and 3 nm of thickness, respectively) was deposited by RF sputtering followed by a lift-off process under sonication.

Claims

CLAIMS1. Computer implemented authentication and / or identification method for an authenti cable object having an engraved surface defining an artificial fingerprint manufactured through a mask originated by block-copolymers, comprising the steps of:● Capturing, e.g. via an atomic force microscopy or scanning electron microscope the artificial fingerprint and elaborating via a binarization algorithm to generate a check image of the artificial fingerprint● Comparing data based on the check image and data based on a binarized fingerprint master image of an authentic fingerprint to define whether the authenticate object is authentic and / or to identify an object.

2. The authentication and / or identification method according to claim 1, further comprising the steps ofProcessing the check image to extract a check binary code matrix from the check image based on defects of the artificial fingerprint, wherein the pixel size is such to achieve a bit uniformity between 0.4 and 0.6Wherein said data based on the check image is a check binary' code matrix and wherein the master binary code is a further matrix having bit uniformity between 0.4 and 0.6, such master matrix being previously extracted from morphologic parameters of a master artificial fingerprint engraved through a block-copolymer mask on an authentic object.

3. Computer implemented method according to claim 1, wherein the binarization algorithm is a lamellae fingerprint enhancement algorithm.

4. Computer implemented method according to claim 1, comprising the further steps of extracting from the check image a limited number of keypoints and apply an homographic transformation to compare said data based on the check image to said data based on the master image.

5. Method for generating data based on a master image of an engraved fingerprint associated to an authentic object or good, comprising the steps of:Molecular self assembly of block copolymers on a surface of the authentic object or goodSelective removal of one of the block copolymers to generate an artificial fingerprint maskEngraving the surface via the mask by chemical or chemical-physical etching to generate an engraved artificial fingerprint on the authentic object or goodCapturing e.g. by AFM or SEM the engraved artificial fingerprint to generate a master raw imageBinarizing the master raw image to provide an binarized master imageStoring said binarized master image with at least an additional feature information of the object or good.

6. Method according to claim 5, further comprising the step of processing, via a microcontroller, the master raw image to extract a master binary code matrix from the master image based on defects of the artificial fingerprints, wherein the pixel size is such as to achieve a bit uniformity between 0.4 and 0.

67. Method according to claims 4 or 5 or 6, wherein the object is a CMOS device.

8. Method according to any of the previous claims, wherein the engraved fingerprint is a lamellae engraved fingerprint.

9. Method comprising the steps of claim 1 and the steps of claim 5.

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