journal article·2023-08Open access

Interfacing Biology and Electronics with Memristive Materials

Ioulia Tzouvadaki, Paschalis Gkoupidenis, Stefano Vassanelli, Shiwei Wang, Themis Prodromakis

Advanced Materials· Volume 35· Issue 32

Abstract

Abstract Memristive technologies promise to have a large impact on modern electronics, particularly in the areas of reconfigurable computing and artificial intelligence (AI) hardware. Meanwhile, the evolution of memristive materials alongside the technological progress is opening application perspectives also in the biomedical field, particularly for implantable and lab‐on‐a‐chip devices where advanced sensing technologies generate a large amount of data. Memristive devices are emerging as bioelectronic links merging biosensing with computation, acting as physical processors of analog signals or in the framework of advanced digital computing architectures. Recent developments in the processing of electrical neural signals, as well as on transduction and processing of chemical biomarkers of neural and endocrine functions, are reviewed. It is concluded with a critical perspective on the future applicability of memristive devices as pivotal building blocks in bio‐AI fusion concepts and bionic schemes.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Photoreceptor and optogenetics research · Neuroscience and Neural Engineering

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Centre for Microsystems Technology Ghent University–IMEC Ghent 9052 Belgium

Max Planck Institute for Polymer Research 55128 Mainz Germany

NeuroChip Laboratory and Padova Neuroscience Centre University of Padova Padova 35129 Italy

Centre for Electronics Frontiers The University of Edinburgh Edinburgh EH9 3JL UK

Funding

Engineering and Physical Sciences Research Council

References (100+)

Show source-provided references
1. Mahmud M. · Front. Neurosci. · 2016 · 10, 438
2. Linares‐Barranco B. · 2009
3. 10.1038/nrd435110.1038/nrd4351 ↗
4. 10.1038/nrneurol.2017.1510.1038/nrneurol.2017.15 ↗
5. 10.1016/j.nbd.2021.10537210.1016/j.nbd.2021.105372 ↗
6. Kandel E. R. · 2021
7. 10.1146/annurev-anchem-061318-11543410.1146/annurev-anchem-061318-115434 ↗
8. 10.1007/s12035-009-8087-710.1007/s12035-009-8087-7 ↗
9. 10.1007/s00216-012-6578-210.1007/s00216-012-6578-2 ↗
10. Campbell M. · 2021
11. 10.1038/s41590-019-0557-010.1038/s41590-019-0557-0 ↗
12. 10.1038/s41928-017-0006-810.1038/s41928-017-0006-8 ↗
13. 10.1038/s41565-018-0302-010.1038/s41565-018-0302-0 ↗
14. 10.1088/0957-4484/22/48/48520310.1088/0957-4484/22/48/485203 ↗
15. 10.1088/0957-4484/22/50/50540210.1088/0957-4484/22/50/505402 ↗
16. 10.1002/adma.20170277010.1002/adma.201702770 ↗
17. 10.1038/nnano.2012.24010.1038/nnano.2012.240 ↗
18. 10.1038/s41598-017-17785-110.1038/s41598-017-17785-1 ↗
19. Krieger C. D. · 2018 · 1
20. 10.3389/fnins.2016.0033310.3389/fnins.2016.00333 ↗
21. 10.1002/adfm.20200677310.1002/adfm.202006773 ↗
22. 10.1002/adfm.20200341910.1002/adfm.202003419 ↗
23. 10.1002/aelm.20160010010.1002/aelm.201600100 ↗
24. 10.1038/s41928-019-0209-210.1038/s41928-019-0209-2 ↗
25. 10.1039/C9MH02033K10.1039/C9MH02033K ↗
26. 10.1002/adma.20110125710.1002/adma.201101257 ↗
27. 10.1038/s41467-020-15759-y10.1038/s41467-020-15759-y ↗
28. 10.1038/nmat501410.1038/nmat5014 ↗
29. 10.1038/s41563-019-0291-x10.1038/s41563-019-0291-x ↗
30. 10.1002/adma.20130244710.1002/adma.201302447 ↗
31. 10.1080/00018732.2010.54496110.1080/00018732.2010.544961 ↗
32. 10.1002/aelm.20180090910.1002/aelm.201800909 ↗
33. 10.1038/s41528-018-0031-310.1038/s41528-018-0031-3 ↗
34. 10.1038/nmat202310.1038/nmat2023 ↗
35. 10.1080/01411594.2011.56147810.1080/01411594.2011.561478 ↗
36. 10.1088/1361-6463/ab779410.1088/1361-6463/ab7794 ↗
37. 10.1088/0957-4484/22/25/25400310.1088/0957-4484/22/25/254003 ↗
38. 10.1038/nnano.2008.16010.1038/nnano.2008.160 ↗
39. 10.1038/nmat161410.1038/nmat1614 ↗
40. 10.1038/nnano.2015.22110.1038/nnano.2015.221 ↗
41. 10.1038/ncomms173710.1038/ncomms1737 ↗
42. 10.1021/nl101473410.1021/nl1014734 ↗
43. 10.1038/s41928-019-0288-010.1038/s41928-019-0288-0 ↗
44. 10.1038/s41928-018-0074-410.1038/s41928-018-0074-4 ↗
45. 10.1038/s41467-020-20519-z10.1038/s41467-020-20519-z ↗
46. 10.1038/s41928-020-0435-710.1038/s41928-020-0435-7 ↗
47. 10.1038/nature1444110.1038/nature14441 ↗
48. 10.1038/nmat351010.1038/nmat3510 ↗
49. 10.1038/nmat475610.1038/nmat4756 ↗
50. 10.1002/aisy.20200008510.1002/aisy.202000085 ↗
51. 10.1038/s41586-020-1942-410.1038/s41586-020-1942-4 ↗
52. b)A.Shafiee A.Nag N.Muralimanohar R.Balasubramonian J. P.Strachan M.Hu R. S.Williams V.Srikumar presented at2016 ACM/IEEE 43rd Annual Int. Symp. Computer Architecture (ISCA) June2016;
53. Chi P. · 2016
54. 10.1145/3297858.330404910.1145/3297858.3304049 ↗
55. 10.1002/adma.20170591410.1002/adma.201705914 ↗
56. 10.1109/TC.2020.299845610.1109/TC.2020.2998456 ↗
57. Liu Q. · 2020 · 1
58. 10.1016/j.isci.2020.10180910.1016/j.isci.2020.101809 ↗
59. 10.1038/nnano.2017.8310.1038/nnano.2017.83 ↗
60. 10.1038/srep1012310.1038/srep10123 ↗
61. 10.1038/s41928-018-0023-210.1038/s41928-018-0023-2 ↗
62. 10.1038/s41928-017-0005-910.1038/s41928-017-0005-9 ↗
63. 10.1016/j.neuron.2020.10.01510.1016/j.neuron.2020.10.015 ↗
64. 10.1109/TBCAS.2019.294307710.1109/TBCAS.2019.2943077 ↗
65. 10.1126/science.abf458810.1126/science.abf4588 ↗
66. Pachitariu M. · 2016
67. 10.1038/ncomms1280510.1038/ncomms12805 ↗
68. 10.1039/C8FD00130H10.1039/C8FD00130H ↗
69. 10.1109/TBCAS.2018.279793910.1109/TBCAS.2018.2797939 ↗
70. 10.1002/admt.20180035010.1002/admt.201800350 ↗
71. 10.1063/1.512415510.1063/1.5124155 ↗
72. 10.1038/s41467-020-16261-110.1038/s41467-020-16261-1 ↗
73. 10.1038/s41598-021-85827-w10.1038/s41598-021-85827-w ↗
74. 10.1038/s41467-021-23342-210.1038/s41467-021-23342-2 ↗
75. Musk E. · bioRxiv · 2019
76. 10.1038/s41551-020-0595-910.1038/s41551-020-0595-9 ↗
77. 10.1038/s41467-020-18105-410.1038/s41467-020-18105-4 ↗
78. 10.1126/sciadv.abc479710.1126/sciadv.abc4797 ↗
79. 10.1109/JSSC.2018.286729310.1109/JSSC.2018.2867293 ↗
80. 10.1109/TBCAS.2019.292545410.1109/TBCAS.2019.2925454 ↗
81. A. M.Hassan A. F.Khalaf K. S.Sayed H. H.Li Y.Chen presented at2018 40th Annual Int. Conf. IEEE Engineering Medicine Biology Society (EMBC) July2018.
82. 10.1039/C4CC09851J10.1039/C4CC09851J ↗
83. 10.1109/TCSII.2014.238765310.1109/TCSII.2014.2387653 ↗
84. 10.1021/jp409257v10.1021/jp409257v ↗
85. 10.1038/s41563-020-0703-y10.1038/s41563-020-0703-y ↗
86. 10.1109/JSEN.2020.304330510.1109/JSEN.2020.3043305 ↗
87. 10.1021/acs.nanolett.6b0164810.1021/acs.nanolett.6b01648 ↗
88. 10.1021/acsomega.0c0079510.1021/acsomega.0c00795 ↗
89. Duh S. H. · TechConnect Briefs · 2014 · 2 · 169
90. Tuoheti A. · Br. J. Cancer · 2020 · 3 · 341
91. Chen E. T. · Sens. Transducers J. · 2014 · 183 · 72
92. 10.1007/s12274-017-1720-210.1007/s12274-017-1720-2 ↗
93. 10.1038/s41598-020-71962-310.1038/s41598-020-71962-3 ↗
94. 10.1109/TCSI.2016.260895910.1109/TCSI.2016.2608959 ↗
95. 10.1039/B204444G10.1039/B204444G ↗
96. 10.1038/nature0693210.1038/nature06932 ↗
97. 10.9770/jesi.2020.8.2(6)10.9770/jesi.2020.8.2(6) ↗
98. 10.1038/s41928-022-00859-y10.1038/s41928-022-00859-y ↗
99. 10.1038/s41928-022-00862-310.1038/s41928-022-00862-3 ↗
100. 10.1038/s41563-022-01450-810.1038/s41563-022-01450-8 ↗