mpw-008 / VIMC Via 1 top, RRAM tier

the VIMC shapes of all of mpw-008

40 of 40 dies rendered, 35 with nothing on this mask.

4,270,659 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
C2 slot-015 tile
leosoc
(empty)
C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4
E4 slot-030 tile
ppcpu2
(empty)
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 (4.5 MB in total, largest 222.3 kB), 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-008_VIMC_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-008_VIMC_reticle.png
the stitched reticle, full resolution
3.7 MB 5ce546b5881930b43c101cfbee58631486a2ffc55718379c7fe51ff6164b2a7bd5a776c6 d635c7fac2855ae10dd0307e7c3ab19e55df1b191977d0c04a3e2a8867f5b643
mpw-008_VIMC_reticle_overview.png
the same image scaled to fit 4096 px
24.0 kB 1fe2473d7309b6d26dcb791b0c6713c0bba141cabb42dfe664c92e459af91da62b8db22d 8fdcd099d38fe5dbbc8b63157679b73bae3746becef54c5e4b7debf57138c5e7
mpw-008_VIMC_tiles.json
where every tile belongs, and its checksum
13.5 kB 766964a1348edbab19ffe81ac179137473c66e0333e7d59a3bdbcf1842a58f067a6ce092 8611ff0115e20bae5935673fedd3a3ab086085f13bcada4b2dc06c477abd7aef
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 wishbone_pio 7576×107760, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A2 iit_indore_mm 7576×107767576, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A4 riscduino-dcore_d4_ 7576×1077622728, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A5 riscduino-qcore_q3_ 7576×1077630304, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A6 caravel_mpw8 7576×1077637880, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 103.2 kB782674c6a93cba4781e5e9d1c9e3c379cdebb96368ed8fe1e2e1c3b0e3a7cf1c
B2 sramtestvehicleg4 7576×107767576, 10776 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
B3 reram_crossbar_rerun 7576×1077615152, 10776 107.5 kBabab4e752713606d027f7f176f461407314e38f17cb23b0ca1fcb963538be89e
B4 tinytapeout_skullart 7576×1077622728, 10776 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
B5 soomrv__mpw-8_ 7576×1077630304, 10776 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
B6 clock_divide_selector 7576×1077637880, 10776 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
B7 reram_module_mpw8 7976×1077645456, 10776 125.8 kB928a6a4bf0ccb7a00467c7a4367b9062754de1a2b58a3d82b168a636da7c4198
C1 shapingthewaves_8 7576×107760, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C2 leosoc 7576×107767576, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C3 decrypt_aes128 7576×1077615152, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C4 approximate_adder_axha 7576×1077622728, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
D2 hyperram_controller_for_sky130 7576×107767576, 32328 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
D3 hardened_alu 7576×1077615152, 32328 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 102.9 kB041f106965e8aeb8f9545cca46402afeec18c3b0a9c3980d23720779732136cd
D6 gf180_vs_sky130 7576×1077637880, 32328 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
E1 iiitb_sd_mpw8 7576×107760, 43104 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
E2 iiitb_sfifo 7576×107767576, 43104 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
E3 iiitb_alu 7576×1077615152, 43104 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
E4 ppcpu2 7576×1077622728, 43104 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
E5 mega4_reram 7576×1077630304, 43104 222.3 kB84bc61f9a7b8d5baa792206d9ec280be7765cb0af4876110cc5b39496c943397
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
E7 sky130cla 7976×1077645456, 43104 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
F1 uetrv_esoc_v2 7576×111760, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F2 sequencedetection1010 7576×111767576, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F3 testasynctrimux-mpw8 7576×1117615152, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F6 arbiter_puf 7576×1117637880, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F7 unigate-sky 7976×1117645456, 53880 110.7 kB4819853e853378d6bf252c7d9d89c4caedfec9d3b73e01c973cd666bce8783c6

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 VIMC on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask VIMC

Mask layer
VIMC — Via 1 top, RRAM tier
Drawn layer
68:44
Derived from drawn layers as
68:44 AND 201:20
Detail
the second via1 mask of the sky130B RRAM tier (r1v). The RRAM splits via1 in two: the designer draws one 'via' shape, and mask generation separates it into via_bot (cviam = VIM) and via_top (r1v) wherever the via is enclosed by r1c. Rendered as via AND r1c. NOT CTM1/mcon, as once assumed: 8016/17/18 each carry a separate CTM1 plate (400), and 455 sits above MM1 (450) while mcon is below met1
Also
sky130B RRAM tier; r1v, the upper half of the split via1
Used in SKY130
no

mpw-008 — SKY130 MPW-8

Reticle set
5CS8017AC
Run date
2022-11-19
Submissions closed
2022-12-31
Projects
144
Identification
inferred — Sequence after 8016=MPW-7 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-8 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-008/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.