mpw-002 / VIM Via

the VIM shapes of all of mpw-002

40 of 40 dies rendered.

78,336,115 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (65.9 MB in total, largest 2.9 MB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-002_VIM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-002_VIM_reticle.png
the stitched reticle, full resolution
34.2 MB b87015b81692caee3a9ab46018c952620e44d91de3a654a7df327ba17bfb8f1411f12110 72ca22962cffb23f47e6c785e0d5db7b6ab2c7f69118b3e0912ff0d626393a86
mpw-002_VIM_reticle_overview.png
the same image scaled to fit 4096 px
800.9 kB 16969f316671e3ca0159263958703b9f29811f8a3eb89bd1b4be7f578c65289811d8fb94 bdefbc0e1d04afbc954f965aa9f437a428d071a8deb45b0d7e33b8c8299526e9
mpw-002_VIM_tiles.json
where every tile belongs, and its checksum
13.3 kB 1a950b12e7dc90dbedba147eb61c791942326f4bfff94d921ba3d63daccb51d312f4dab5 93f0bef78512852af9892b2f38509251c0f5fb57b7f4e30da8388aa2f0859483
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 axi_dma_using_spinal 7576×107760, 0 1.9 MB8a916ca45541c31395bf711e72be7bfcc63690a672f6dbcfccf3cf85ac1b2aaa
A2 wishbone_can 7576×107767576, 0 2.0 MBdc967475077b3072e6400ff406434e91de747b96e8497270603bc7b57cf1e97e
A3 Analog-Neural-Network 7576×1077615152, 0 1.3 MB9a7562d52c50de5c0c8d5099d03f73925b2ba149c4e8436166f29b037535b329
A4 caravel_dsp2 7576×1077622728, 0 1.7 MB09e4998038e8a3cae8be6b69933624c7dbff39e7aab34f35d6f8ce85da992456
A5 eFPGA_v3_caravel 7576×1077630304, 0 1.8 MB0e632d72d3096a2f5b2f5619a18598fd2ac6aa0e39bc0002432eb6955cc64edb
A6 opencryo_testchip 7576×1077637880, 0 1.3 MB8d02a57c05aee4e0ff1579a1d03c64b4dbce6f4293098ca10e5558d0757f22ab
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 2.2 MB3ad1c95cbcf803c7c27c12491dfdd07befd8f6d1d0d819ae649d220189863cae
B2 lexicon 7576×107767576, 10776 1.5 MBbb08c2e8a012ea7ef9d4eeecaecf67ea8881f18888e6d308c220530e3f9f710d
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 1.6 MB8a016be474cf426f0e5683729dcd962434d3412c61fdeb8557920af5ae801b37
B4 yonga-lz4_decoder 7576×1077622728, 10776 1.6 MBf99a4ce7c3554c2fc652a75c65aacf28909a6c6efd018c43278fd2976c8c8b87
B5 Potentiometric_Digit 7576×1077630304, 10776 1.3 MB4ddc5c618676ebb45cd12c8621db676aa077a98ccde9b91a977bab6ed2198399
B6 fuserisc_ver2 7576×1077637880, 10776 1.7 MB6e95339de8e3ab4544b8e550be65e517df64deae8692c0a0b0cc7616e2716760
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 1.4 MB1389a7a6a28dfb6d79809147a47f25bc8c9fac5970f0ce587760f027deb35c37
C1 Comparator_3v3 7576×107760, 21552 1.3 MB50dc691aac762faca1146d366f765b9688d208e42314c50a64ac0cbe83dd4b0f
C2 caravel_multi_encode 7576×107767576, 21552 1.5 MBc347136038ad19a325dc2beb2bb29d864820162c5bb686a9d87983082cb06a0a
C3 kasirgalabs_uart 7576×1077615152, 21552 1.5 MB06cc3e17fb7ed77e18dc773c0eb66a7a32e34df6cfb946cdb0cd2f16a137c8be
C4 Automatically_genera 7576×1077622728, 21552 1.5 MBa317aa8817cd9da466a2427a6de3cd8201b8af89a95800f6bd0b49bc3ed13cb6
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 1.5 MB8eff28828c497b082e8430145648c1dce5bfed74765c687dddd2d3b9a5ce3e31
C7 caravel_azadi_soc 7976×1077645456, 21552 2.3 MBdf41d788825c3a9b67822c99928467bef841b0847ac6add93df84021ec13cd1e
D1 caravel_dsc 7576×107760, 32328 1.7 MB4c15527df830fdbc4a0eaf136f8eeea647e05aedc316d3b76020d235fb0dd6ac
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 1.6 MBd0a466950aa9cca1b38b3a2e23fb8a0d328fb06a20c68c96692e2c6ce0e17a58
D3 caravel_user_project 7576×1077615152, 32328 1.5 MBee6a9e77a015ae008db12ec852588d959d7bf2013afeb98cdde26554d2865108
D4 General_Purpose_Band 7576×1077622728, 32328 1.4 MB1847d64c037b40af8a0e8e87505f6558cfea8a5ae5b9bb1d25064a619f287bbf
D5 zero_to_asic 7576×1077630304, 32328 2.0 MB70f0523c3e703d744c10ed4c7468e0c0e2038b36d2e49b8c10ce6dba32fc4b80
D6 fast_gcd_for_large_i 7576×1077637880, 32328 1.5 MBaa6aea7623a5bc42845231de6f26995dfd0abaac34dd835c960afd1ff2de3c6c
D7 Columbus 7976×1077645456, 32328 1.3 MB1528a3cbdc9d93fc0b64c67eea2211879231082c06edabbc7ab354f0f98ebf41
E1 Space_shuttle 7576×107760, 43104 2.5 MB8209a71720c48c9178b7ba58f5a143812f5e2373ae5940c8e66e05481e13b69a
E2 subservient 7576×107767576, 43104 1.7 MB64c91a98aac97d97671c26f767ef905ead844d41960f62675b719f87e1b75914
E3 caravel_analog_fulgor 7576×1077615152, 43104 1.3 MB3e56f1b60607e1263bf56ac25fa92c7c70636b1b6417e0c4b7a8cb88958d0729
E4 bandgap_reference_de 7576×1077622728, 43104 1.4 MB4822fae707c673c43c73aff6c54e58724a04da71f867508aef914ee9f9efdd2b
E5 renml 7576×1077630304, 43104 2.9 MB2604c30fec45b0b95cb1f55a83653400852f0ce66b16508ba356e3a190be71b9
E6 io_expander 7576×1077637880, 43104 1.5 MBc803c1709b84b8613d5e36ad93c8259c5cb1075f9e84e74974a5bc0d47f79ce9
E7 digital_pll 7976×1077645456, 43104 1.3 MB751053a27e3e7ffcae5c5704cfbfea096df4c996a85002f952fd87f3309ac815
F1 zero_to_asic 7576×111760, 53880 2.0 MB9c63a1411f753c2b25690e469764a964672b4e635a5ac588f71ced4d4e38872a
F2 caravel_fpu 7576×111767576, 53880 1.7 MB20aa4ced77d8ba1ba8e4add1f45cbfda3c215c0f81b387184170d441e3ae08a5
F3 BrqRV_EB1 7576×1117615152, 53880 1.5 MBcdfa76668e17378bbfad7b99e899dcc38a0a743cc0adaa0ad009e91aefc35e5c
F4 hilas 7576×1117622728, 53880 1.3 MB66351ccc07081d38d5c5df00c87cff436fa073e2a6590abf2df58979da8e547a
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 1.3 MBb74380ba5393ae9d585e6f71f485835c04bf41dac3330bc98686bc592d5f8037
F6 sha1_engine 7576×1117637880, 53880 1.8 MB2c9fc4f062d0bfac7c9b828019b9a74d9e0c4a2c78a7452049bc098b1248811d
F7 caravan_openfasoc 7976×1117645456, 53880 1.5 MB31411d93d56555b0f8a4566b04707a9f7590d512925d30fb3f8b215b66a921a9

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About VIM on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask VIM

Mask layer
VIM — Via
Process step
118 — Via1 mask
Drawn layer
68:44
Layer on the mask-layer GDS
40:0
Detail
in the sky130B RRAM tier this is cviam, the lower half of the split via1; the upper half is VIMC (r1v)
Also
Via 1 (M1→M2)
Used in SKY130
yes

mpw-002 — SKY130 MPW-2

Foundry product
EF MPW#2
Reticle set
5CS8007AC
Run date
2021-06-18
Submissions closed
2021-06-18
Projects
56
Fab lot
4205819
Identification
confirmed — Lot records: EF MPW#2, lot 4205819 = 'SKY130 MPW 2'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-2 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-002/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.